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  • Global optimization  (158)
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  • Springer  (158)
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  • 1
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    BIT 40 (2000), S. 291-313 
    ISSN: 1572-9125
    Keywords: Global optimization ; interval arithmetic ; range of functions
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract In this paper we introduce a new notion which we call convex-concave extensions. Convex-concave extensions provide for given nonlinear functions convex lower bound functions and concave upper bound functions, and can be viewed as a generalization of interval extensions. Convex-concave extensions can approximate the shape of a given function in a better way than interval extensions which deliver only constant lower and upper bounds for the range. Therefore, convex-concave extensions can be applied in a more flexible manner. For example, they can be used to construct convex relaxations. Moreover, it is demonstrated that in many cases the overestimation which is due to interval extensions can be drastically reduced. Applications and some numerical examples, including constrained global optimization problems of large scale, are presented.
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    Journal of global optimization 18 (2000), S. 367-383 
    ISSN: 1573-2916
    Keywords: Global optimization ; Lennard–Jones clusters ; Basin-hopping ; Energy landscape ; Folding funnel ; Molecular conformation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Molecular conformation problems arising in computational chemistry require the global minimization of a non-convex potential energy function representing the interactions of, for example, the component atoms in a molecular system. Typically the number of local minima on the potential energy surface grows exponentially with system size, and often becomes enormous even for relatively modestly sized systems. Thus the simple multistart strategy of randomly sampling local minima becomes impractical. However, for many molecular conformation potential energy surfaces the local minima can be organized by a simple adjacency relation into a single or at most a small number of funnels. A distinguished local minimum lies at the bottom of each funnel and a monotonically descending sequence of adjacent local minima connects every local minimum in the funnel with the funnel bottom. Thus the global minimum can be found among the comparatively small number of funnel bottoms, and a multistart strategy based on sampling funnel bottoms becomes viable. In this paper we present such an algorithm of the basin-hopping type and apply it to the Lennard–Jones cluster problem, an intensely studied molecular conformation problem which has become a benchmark for global optimization algorithms. Results of numerical experiments are presented which confirm both the multifunneling character of the Lennard–Jones potential surface as well as the efficiency of the algorithm. The algorithm has found all of the current putative global minima in the literature up to 110 atoms, as well as discovered a new global minimum for the 98-atom cluster of a novel geometrical class.
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  • 3
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    Journal of global optimization 16 (2000), S. 245-255 
    ISSN: 1573-2916
    Keywords: Bilevel linear programming ; Global optimization ; Penalty function
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
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  • 4
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    Journal of global optimization 17 (2000), S. 267-283 
    ISSN: 1573-2916
    Keywords: Combinatorial optimization ; Global optimization ; GRASP ; Heuristics ; Local search ; Network design ; Steiner problem in graphs
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract In this paper, we present a parallel greedy randomized adaptive search procedure (GRASP) for the Steiner problem in graphs. GRASP is a two-phase metaheuristic. In the first phase, solutions are constructed using a greedy randomized procedure. Local search is applied in the second phase, leading to a local minimum with respect to a specified neighborhood. In the Steiner problem in graphs, feasible solutions can be characterized by their non-terminal nodes (Steiner nodes) or by their key-paths. According to this characterization, two GRASP procedures are described using different local search strategies. Both use an identical construction procedure. The first uses a node-based neighborhood for local search, while the second uses a path-based neighborhood. Computational results comparing the two procedures show that while the node-based variant produces better quality solutions, the path-based variant is about twice as fast. A hybrid GRASP procedure combining the two neighborhood search strategies is then proposed. Computational experiments with a parallel implementation of the hybrid procedure are reported, showing that the algorithm found optimal solutions for 45 out of 60 benchmark instances and was never off by more than 4% of the optimal solution value. The average speedup results observed for the test problems show that increasing the number of processors reduces elapsed times with increasing speedups. Moreover, the main contribution of the parallel algorithm concerns the fact that larger speedups of the same order of the number of processors are obtained exactly for the most difficult problems.
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    Journal of global optimization 16 (2000), S. 371-392 
    ISSN: 1573-2916
    Keywords: Branch-and-bound method ; Global optimization ; Interval arithmetic ; Multisection ; Accelerating devices
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract We have investigated variants of interval branch-and-bound algorithms for global optimization where the bisection step was substituted by the subdivision of the current, actual interval into many subintervals in a single iteration step. The convergence properties of the multisplitting methods, an important class of multisection procedures are investigated in detail. We also studied theoretically the convergence improvements caused by multisection on algorithms which involve the accelerating tests (like e.g. the monotonicity test). The results are published in two papers, the second one contains the numerical test result.
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  • 6
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    Journal of global optimization 16 (2000), S. 301-323 
    ISSN: 1573-2916
    Keywords: Outcome polyhedron ; Linear programming ; Pivoting ; Nonlinear programming ; Global optimization ; Extreme point mathematical programming ; Neighborhood problem
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract In many types of linear, convex and nonconvex optimization problems over polyhedra, a global optimal solution can be found by searching the extreme points of the outcome polyhedron Y instead of the extreme points of the decision set polyhedron Z. Since the dimension of Y is often significantly smaller than the dimension of Z, and since the structure of Y is often much simpler than the structure of Z, such an approach has the potential to often yield significant computational savings. This article seeks to motivate these potential savings through both general theory and concrete examples. The article then develops two new procedures. The first procedure is linear-programming based and finds an initial extreme point of an outcome polyhedron Y. The second procedure provides a mechanism for moving from a given extreme point y of Y along any chosen edge of Y emanating from y until a neighboring extreme point to y is reached. As a by-product of the second procedure, as in the pivoting process of the simplex method, a complete algebraic description of the chosen edge can also be easily obtained.
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  • 7
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    Journal of global optimization 17 (2000), S. 161-165 
    ISSN: 1573-2916
    Keywords: Global optimization ; Gradient flow ; Min-max graphs
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Let f be a smooth nondegenerate real valued function on a finite dimensional, compact and connected Riemannian manifold. The bipartite min-max graph Γ is defined as follows. Its nodes are formed by the set of local minima and the set of local maxima. Two nodes (a local minimum and a local maximum) are connected in Γ by means of an edge if some trajectory of the corresponding gradient flow connects them. Given a natural number k, we construct a function f such that the length of the shortest path in Γ between two specific local minima exceeds k. The latter construction is independent of the underlying Riemannian metric.
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    Journal of global optimization 17 (2000), S. 97-126 
    ISSN: 1573-2916
    Keywords: Differential-algebraic equations ; Global optimization ; Optimal control
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract The accurate solution of optimal control problems is crucial in many areas of engineering and applied science. For systems which are described by a nonlinear set of differential-algebraic equations, these problems have been shown to often contain multiple local minima. Methods exist which attempt to determine the global solution of these formulations. These algorithms are stochastic in nature and can still get trapped in local minima. There is currently no deterministic method which can solve, to global optimality, the nonlinear optimal control problem. In this paper a deterministic global optimization approach based on a branch and bound framework is introduced to address the nonlinear optimal control problem to global optimality. Only mild conditions on the differentiability of the dynamic system are required. The implementa-tion of the approach is discussed and computational studies are presented for four control problems which exhibit multiple local minima.
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    Journal of global optimization 17 (2000), S. 127-160 
    ISSN: 1573-2916
    Keywords: Bi-duality ; Canonical dual transformation ; D.C. optimization ; Duality ; Global optimization ; Nonconvexity ; Nonsmoothness ; Reformulation ; Triality
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper presents, within a unified framework, a potentially powerful canonical dual transformation method and associated generalized duality theory in nonsmooth global optimization. It is shown that by the use of this method, many nonsmooth/nonconvex constrained primal problems in ℝ n can be reformulated into certain smooth/convex unconstrained dual problems in ℝ m with m ⩽ n and without duality gap, and some NP-hard concave minimization problems can be transformed into unconstrained convex minimization dual problems. The extended Lagrange duality principles proposed recently in finite deformation theory are generalized suitable for solving a large class of nonconvex and nonsmooth problems. The very interesting generalized triality theory can be used to establish nice theoretical results and to develop efficient alternative algorithms for robust computations.
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  • 10
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    Journal of global optimization 16 (2000), S. 219-228 
    ISSN: 1573-2916
    Keywords: Branch-and-bound method ; Global optimization ; Interval arithmetic ; Multisection ; Accelerating devices
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract We have investigated variants of interval branch-and-bound algorithms for global optimization where the bisection step was substituted by the subdivision of the current, actual interval into many subintervals in a single iteration step. The results are published in two papers, the first one contains the theoretical investigations on the convergence properties. An extensive numerical study indicates that multisection can substantially improve the efficiency of interval global optimization procedures, and multisection seems to be indispensable in solving hard global optimization problems in a reliable way.
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  • 11
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    Journal of global optimization 16 (2000), S. 355-369 
    ISSN: 1573-2916
    Keywords: Global optimization ; Parametric monotone composition ; Convexification ; Optimality condition ; Least root problem
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract In this paper a successive optimization method for solving inequality constrained optimization problems is introduced via a parametric monotone composition reformulation. The global optimal value of the original constrained optimization problem is shown to be the least root of the optimal value function of an auxiliary parametric optimization problem, thus can be found via a bisection method. The parametric optimization subproblem is formulated in such a way that it is a one-parameter problem and its value function is a monotone composition function with respect to the original objective function and the constraints. Various forms can be taken in the parametric optimization problem in accordance with a special structure of the original optimization problem, and in some cases, the parametric optimization problems are convex composite ones. Finally, the parametric monotone composite reformulation is applied to study local optimality.
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  • 12
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    Journal of global optimization 18 (2000), S. 59-73 
    ISSN: 1573-2916
    Keywords: Global optimization ; Production-transportation problem ; Minimum concave-cost flow problem ; Branch-and-bound algorithm ; Lagrangian relaxation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract We propose a branch-and-bound algorithm of Falk–Soland's type for solving the minimum cost production-transportation problem with concave production costs. To accelerate the convergence of the algorithm, we reinforce the bounding operation using a Lagrangian relaxation, which is a concave minimization but yields a tighter bound than the usual linear programming relaxation in O(mn log n) additional time. Computational results indicate that the algorithm can solve fairly large scale problems.
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    Journal of global optimization 16 (2000), S. 325-341 
    ISSN: 1573-2916
    Keywords: Global optimization ; Lennard-Jones clusters ; Asymptotic convergence
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract We have designed a simple method to place particles on lattices, concentric shells and icosahedral concentric layers for minimizing the total energy of Lennard-Jones clusters, approximately, by analytical means. The most significant difference of our schemes from others is the dramatic reduction of parameters, which allows the study of large clusters, not possible otherwise. We present the derivation of formulae for minimal per-particle energy and for inter-particle distance. We also present their asymptotic values for large number of particles.
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    Journal of global optimization 18 (2000), S. 337-356 
    ISSN: 1573-2916
    Keywords: Global optimization ; Nonconvex quadratic programming ; Lagrangian relaxation ; Optimality cuts ; Duality gap
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract A central problem of branch-and-bound methods for global optimization is that often a lower bound do not match with the optimal value of the corresponding subproblem even if the diameter of the partition set shrinks to zero. This can lead to a large number of subdivisions preventing the method from terminating in reasonable time. For the all-quadratic optimization problem with convex constraints we present optimality cuts which cut off a given local minimizer from the feasible set. We propose a branch-and-bound algorithm using optimality cuts which is finite if all global minimizers fulfill a certain second order optimality condition. The optimality cuts are based on the formulation of a dual problem where additional redundant constraints are added. This technique is also used for constructing tight lower bounds. Moreover we present for the box-constrained and the standard quadratic programming problem dual bounds which have under certain conditions a zero duality gap.
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  • 15
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    Journal of global optimization 14 (1999), S. 55-78 
    ISSN: 1573-2916
    Keywords: Adaptation ; Clustering ; Covering ; Descent ; Global optimization ; Parameter identification
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Two strategies of randomized search, namely adaptive cluster covering (ACCO), and adaptive cluster covering with descent (ACD), are introduced and positioned in the group of the global optimization techniques. Several algorithms based on these new strategies are compared with other techniques of global randomized search in terms of effectiveness, efficiency and reliability. The other techniques include two versions of multistart, two versions of the controlled random search (CRS2 and CRS4) and the canonical genetic algorithm. Thirteen minimization problems including two parameter identification problems (for a flexible membrane mirror model and a hydrologic model) are solved. The algorithm ACCO, and a version of CRS4 algorithm (Ali and Storey 1994) show the highest efficiency, effectiveness and reliability. The second new algorithm, ACD, is in some runs very efficient and effective, but its reliability needs further improvement.
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  • 16
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    Journal of global optimization 14 (1999), S. 157-179 
    ISSN: 1573-2916
    Keywords: Global optimization ; Interval methods ; Local nonsmooth optimization ; Scientific computing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract An interval method for determining local solutions of nonsmooth unconstrained optimization problems is discussed. The objective function is assumed to be locally Lipschitz and to have appropriate interval inclusions. The method consists of two parts, a local search and a global continuation and termination. The local search consists of a globally convergent descent algorithm showing similarities to ε-bundle methods. While ε-bundle methods use polytopes as inner approximations of the ε-subdifferentials, which are the main tools of almost all bundle concepts, our method uses axes parallel boxes as outer approximations of the ε-subdifferentials. The boxes are determined almost automatically with inclusion techniques of interval arithmetic. The dimension of the boxes is equal to the dimension of the problem and remains constant during the whole computation. The application of boxes does not suffer from the necessity to invest methodical and computational efforts to adapt the polytopes to the latest state of the computation as well as to simplify them when the number of vertices becomes too large, as is the case with the polytopes. The second part of the method applies interval techniques of global optimization to the approximative local solution obtained from the search of the first part in order to determine guaranteed error bounds or to improve the solution if necessary. We present prototype algorithms for both parts of the method as well as a complete convergence theory for them and demonstrate how outer approximations can be obtained.
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  • 17
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    Journal of global optimization 14 (1999), S. 205-216 
    ISSN: 1573-2916
    Keywords: Global optimization ; Lipschitz continuity ; Piyavskii's algorithm ; Uniform Continuity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract We use the simple, but little-known, result that a uniformly continuous function on a convex set is ε-Lipschitz (as defined below) to extend Piyavskii's algorithm for Lipschitz global optimization to the larger domain of continuous (not-necessarily-Lipschitz) global optimization.
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    Journal of global optimization 14 (1999), S. 365-393 
    ISSN: 1573-2916
    Keywords: Branch-and-bound ; Global optimization ; Interval arithmetic ; Interval slopes
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    Topics: Mathematics
    Notes: Abstract In this paper we introduce a pruning technique based on slopes in the context of interval branch-and-bound methods for nonsmooth global optimization. We develop the theory for a slope pruning step which can be utilized as an accelerating device similar to the monotonicity test frequently used in interval methods for smooth problems. This pruning step offers the possibility to cut away a large part of the box currently investigated by the optimization algorithm. We underline the new technique's efficiency by comparing two variants of a global optimization model algorithm: one equipped with the monotonicity test and one equipped with the pruning step. For this reason, we compared the required CPU time, the number of function and derivative or slope evaluations, and the necessary storage space when solving several smooth global optimization problems with the two variants. The paper concludes on the test results for several nonsmooth examples.
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    Journal of global optimization 14 (1999), S. 357-364 
    ISSN: 1573-2916
    Keywords: Global optimization ; Nonconvex quadratic programming ; Semidefinite programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract The paper describes a method for computing a lower bound of the global minimum of an indefinite quadratic form over a simplex. The bound is derived by computing an underestimator of the convex envelope by solving a semidefinite program (SDP). This results in a convex quadratic program (QP). It is shown that the optimal value of the QP is a lower bound of the optimal value of the original problem. Since there exist fast (polynomial time) algorithms for solving SDP's and QP's the bound can be computed in reasonable time. Numerical experiments indicate that the relative error of the bound is about 10 percent for problems up to 20 variables, which is much better than a known SDP bound.
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    Journal of global optimization 14 (1999), S. 437-447 
    ISSN: 1573-2916
    Keywords: Global optimization ; Problem features ; Problem classes ; Test problems ; Solution techniques
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract There is a lack of a representative set of test problems for comparing global optimization methods. To remedy this a classification of essentially unconstrained global optimization problems into unimodal, easy, moderately difficult, and difficult problems is proposed. The problem features giving this classification are the chance to miss the region of attraction of the global minimum, embeddedness of the global minimum, and the number of minimizers. The classification of some often used test problems are given and it is recognized that most of them are easy and some even unimodal. Global optimization solution techniques treated are global, local, and adaptive search and their use for tackling different classes of problems is discussed. The problem of fair comparison of methods is then adressed. Further possible components of a general global optimization tool based on the problem classes and solution techniques is presented.
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    Journal of optimization theory and applications 102 (1999), S. 479-495 
    ISSN: 1573-2878
    Keywords: Global optimization ; Gaussian processes
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    Topics: Mathematics
    Notes: Abstract The Wiener process is a widely used statistical model for stochastic global optimization. One of the first optimization algorithms based on a statistical model, the so-called P-algorithm, was based on the Wiener process. Despite many advantages, this process does not give a realistic model for many optimization problems, particularly from the point of view of local behavior. In the present paper, a version of the P-algorithm is constructed based on a stochastic process with smooth sampling functions. It is shown that, in such a case, the algorithm has a better convergence rate than in the case of the Wiener process. A similar convergence rate is proved for a combination of the Wiener model-based P-algorithm with quadratic fit-based local search.
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    Journal of global optimization 15 (1999), S. 19-39 
    ISSN: 1573-2916
    Keywords: Abstract convexity ; Global optimization ; Increasing function
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    Topics: Mathematics
    Notes: Abstract We study a broad class of increasing non-convex functions whose level sets are star shaped with respect to infinity. We show that these functions (we call them ISSI functions) are abstract convex with respect to the set of min-type functions and exploit this fact for their minimization. An algorithm is proposed for solving global optimization problems with an ISSI objective function and its numerical performance is discussed.
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    Journal of global optimization 15 (1999), S. 157-167 
    ISSN: 1573-2916
    Keywords: Global optimization ; Parallel computations ; Local tuning
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    Topics: Mathematics
    Notes: Abstract In this paper we propose a new parallel algorithm for solving global optimization (GO) multidimensional problems. The method unifies two powerful approaches for accelerating the search: parallel computations and local tuning on the behavior of the objective function. We establish convergence conditions for the algorithm and theoretically show that the usage of local information during the global search permits to accelerate solving the problem significantly. Results of numerical experiments executed with 100 test functions are also reported.
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    Journal of global optimization 15 (1999), S. 315-342 
    ISSN: 1573-2916
    Keywords: Multiplicative programming ; Convex multiplicative programming ; Global optimization ; Outer approximation ; Branch and bound ; Nonconvex programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This article presents a new global solution algorithm for Convex Multiplicative Programming called the Outcome Space Algorithm. To solve a given convex multiplicative program (P D), the algorithm solves instead an equivalent quasiconcave minimization problem in the outcome space of the original problem. To help accomplish this, the algorithm uses branching, bounding and outer approximation by polytopes, all in the outcome space of problem (P D). The algorithm economizes the computations that it requires by working in the outcome space, by avoiding the need to compute new vertices in the outer approximation process, and, except for one convex program per iteration, by requiring for its execution only linear programming techniques and simple algebra.
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    Journal of global optimization 14 (1999), S. 267-281 
    ISSN: 1573-2916
    Keywords: Concave programming problem ; Cutting plane method ; Global optimization ; Outer approximation method ; Supporting hyperplane method
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    Topics: Mathematics
    Notes: Abstract We treat a concave programming problem with a compact convex feasible set. Assuming the differentiability of the convex functions which define the feasible set, we propose two solution methods. Those methods utilize the convexity of the feasible set and the property of the normal cone to the feasible set at each point over the boundary. Based on the proposed two methods, we propose a solution algorithm. This algorithm takes advantages over classical methods: (1) the obtained approximate solution is always feasible, (2) the error of such approximate value can be evaluated properly for the optimal value of such problem, (3) the algorithm does not have any redundant iterations.
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    Journal of global optimization 14 (1999), S. 299-312 
    ISSN: 1573-2916
    Keywords: Computational biology ; Global optimization ; Molecular similarity
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    Topics: Mathematics
    Notes: Abstract Molecular similarity index measures the similarity between two molecules. Computing the optimal similarity index is a hard global optimization problem. Since the objective function value is very hard to compute and its gradient vector is usually not available, previous research has been based on non-gradient algorithms such as random search and the simplex method. In a recent paper, McMahon and King introduced a Gaussian approximation so that both the function value and the gradient vector can be computed analytically. They then proposed a steepest descent algorithm for computing the optimal similarity index of small molecules. In this paper, we consider a similar problem. Instead of computing atom-based derivatives, we directly compute the derivatives with respect to the six free variables describing the relative positions of the two molecules.. We show that both the function value and gradient vector can be computed analytically and apply the more advanced BFGS method in addition to the steepest descent algorithm. The algorithms are applied to compute the similarities among the 20 amino acids and biomolecules like proteins. Our computational results show that our algorithm can achieve more accuracy than previous methods and has a 6-fold speedup over the steepest descent method.
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    Journal of global optimization 14 (1999), S. 331-355 
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    Keywords: Global optimization ; Bound constraints ; Local optimization ; Coordinate search
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    Topics: Mathematics
    Notes: Abstract Inspired by a method by Jones et al. (1993), we present a global optimization algorithm based on multilevel coordinate search. It is guaranteed to converge if the function is continuous in the neighborhood of a global minimizer. By starting a local search from certain good points, an improved convergence result is obtained. We discuss implementation details and give some numerical results.
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    Journal of global optimization 15 (1999), S. 235-260 
    ISSN: 1573-2916
    Keywords: Global optimization ; Multiple-minima problem ; Protein folding ; Structure prediction
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    Topics: Mathematics
    Notes: Abstract Protein folding is a very difficult global optimization problem. Furthermore it is coupled with the difficult task of designing a reliable force field with which one has to search for the global minimum. A summary of a series of optimization methods developed and applied to various problems involving polypeptide chains is described in this paper. With recent developments, a computational treatment of the folding of globular proteins of up to 140 residues is shown to be tractable.
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    Mathematical programming 81 (1998), S. 127-146 
    ISSN: 1436-4646
    Keywords: Global optimization ; Lipschitzean first derivatives ; Numerical algorithms ; Convergence
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract In this paper new global optimization algorithms are proposed for solving problems where the objective function is univariate and has Lipschitzean first derivatives. To solve this problem, smooth auxiliary functions, which are adaptively improved during the course of the search, are constructed. Three new algorithms are introduced: the first used the exact a priori known Lipschitz constant for derivatives; the second, when this constant is unknown, estimates it during the course of the search and finally, the last method uses neither the exact global Lipschitz constant nor its estimate but instead adaptively estimates the local Lipschitz constants in different sectors of the search region during the course of optimization. Convergence conditions of the methods are investigated from a general viewpoint and some numerical results are also given. © 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.
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    Journal of global optimization 13 (1998), S. 433-444 
    ISSN: 1573-2916
    Keywords: Adaptive search ; Global optimization ; Multi-disciplinary optimization ; Simulated annealing
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    Topics: Mathematics
    Notes: Abstract Engineering design problems often involve global optimization of functions that are supplied as ‘black box’ functions. These functions may be nonconvex, nondifferentiable and even discontinuous. In addition, the decision variables may be a combination of discrete and continuous variables. The functions are usually computationally expensive, and may involve finite element methods. An engineering example of this type of problem is to minimize the weight of a structure, while limiting strain to be below a certain threshold. This type of global optimization problem is very difficult to solve, yet design engineers must find some solution to their problem – even if it is a suboptimal one. Sometimes the most difficult part of the problem is finding any feasible solution. Stochastic methods, including sequential random search and simulated annealing, are finding many applications to this type of practical global optimization problem. Improving Hit-and-Run (IHR) is a sequential random search method that has been successfully used in several engineering design applications, such as the optimal design of composite structures. A motivation to IHR is discussed as well as several enhancements. The enhancements include allowing both continuous and discrete variables in the problem formulation. This has many practical advantages, because design variables often involve a mixture of continuous and discrete values. IHR and several variations have been applied to the composites design problem. Some of this practical experience is discussed.
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    Journal of global optimization 12 (1998), S. 267-283 
    ISSN: 1573-2916
    Keywords: Polynomial programs ; Reformulation-Linearization Technique (RLT) ; Nonconvex programming ; Global optimization
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    Topics: Mathematics
    Notes: Abstract This paper considers the solution of nonconvex polynomial programming problems that arise in various engineering design, network distribution, and location-allocation contexts. These problems generally have nonconvex polynomial objective functions and constraints, involving terms of mixed-sign coefficients (as in signomial geometric programs) that have rational exponents on variables. For such problems, we develop an extension of the Reformulation-Linearization Technique (RLT) to generate linear programming relaxations that are embedded within a branch-and-bound algorithm. Suitable branching or partitioning strategies are designed for which convergence to a global optimal solution is established. The procedure is illustrated using a numerical example, and several possible extensions and algorithmic enhancements are discussed.
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    Journal of global optimization 12 (1998), S. 353-372 
    ISSN: 1573-2916
    Keywords: Multiple objective linear programming ; Optimization over the efficient set ; Interactive methods ; Global optimization ; Citrus rootstockselection
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    Notes: Abstract A multiple objective linear programming problem (P) involves the simultaneous maximization of two or more conflicting linear objective functions over a nonempty polyhedron X. Many of the most popular methods for solving this type of problem, including many well-known interactive methods, involve searching the efficient set X E of the problem. Generally, however, X E is a complicated, nonconvex set. As a result, concepts and methods from global optimization may be useful in searching X E. In this paper, we will explain in theory, and show via an actual application to citrus rootstock selection in Florida, how the potential usefulness of the well-known interactive method STEM for solving problem (P) in this way, can depend crucially upon how accurately certain global optimization problems involving minimizations over X E are solved. In particular, we will show both in theory and in practice that the choice of whether to use the popular but unreliable ‘payoff table’ approach or to use one of the lesser known, more accurate global optimization methods to solve these problems can determine whether STEM succeeds or fails as a decision aid. Several lessons and conclusions of transferable value derived from this research are also given.
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    Journal of global optimization 12 (1998), S. 325-351 
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    Keywords: Chemical and phase equilibrium ; convexity ; Gibbs free energy ; Global optimization ; Non-convex optimization ; Tangent-plane criterion
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    Notes: Abstract This paper addresses the problem of finding the number, K, of phases present at equilibrium and their composition, in a chemical mixture of n s substances. This corresponds to the global minimum of the Gibbs free energy of the system, subject to constraints representing m b independent conserved quantities, where m b=n s when no reaction is possible and m b ≤ n e +1 when reaction is possible and n e is the number of elements present. After surveying previous work in the field and pointing out the main issues, we extend the necessary and sufficient condition for global optimality based on the ‘reaction tangent-plane criterion’, to the case involving different thermodynamical models (multiple phase classes). We then present an algorithmic approach that reduces this global optimization problem (involving a search space of m b(n s-1) dimensions) to a finite sequence of local optimization steps inK(n s-1) -space, K ≤ m b, and global optimization steps in (n s-1)-space. The global step uses the tangent-plane criterion to determine whether the current solution is optimal, and, if it is not, it finds an improved feasible solution either with the same number of phases or with one added phase. The global step also determines what class of phase (e.g. liquid or vapour) is to be added, if any phase is to be added. Given a local minimization procedure returning a Kuhn–Tucker point and a global optimization procedure (for a lower-dimensional search space) returning a global minimum, the algorithm is proved to converge to a global minimum in a finite number of the above local and global steps. The theory is supported by encouraging computational results.
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    Journal of optimization theory and applications 96 (1998), S. 575-588 
    ISSN: 1573-2878
    Keywords: Global optimization ; convergence ; stochastic algorithms ; deterministic algorithms
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    Notes: Abstract There are many global optimization algorithms which do not use global information. We broaden previous results, showing limitations on such algorithms, even if allowed to run forever. We show that deterministic algorithms must sample a dense set to find the global optimum value and can never be guaranteed to converge only to global optimizers. Further, analogous results show that introducing a stochastic element does not overcome these limitations. An example is simulated annealing in practice. Our results show that there are functions for which the probability of success is arbitrarily small.
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    Journal of global optimization 12 (1998), S. 1-36 
    ISSN: 1573-2916
    Keywords: Global optimization ; concave programming ; branch-and-bound ; domainreduction
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    Notes: Abstract Researchers first examined the problem of separable concave programming more than thirty years ago, making it one of the earliest branches of nonlinear programming to be explored. This paper proposes a new algorithm that finds the exact global minimum of this problem in a finite number of iterations. In addition to proving that our algorithm terminates finitely, the paper extends a guarantee of finiteness to all branch-and-bound algorithms for concave programming that (1) partition exhaustively using rectangular subdivisions and (2) branch on the incumbent solution when possible. The algorithm uses domain reduction techniques to accelerate convergence; it solves problems with as many as 100 nonlinear variables, 400 linear variables and 50 constraints in about five minutes on an IBM RS/6000 Power PC. An industrial application with 152 nonlinear variables, 593 linear variables, and 417 constraints is also solved in about ten minutes.
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    Journal of global optimization 12 (1998), S. 247-265 
    ISSN: 1573-2916
    Keywords: Global optimization ; Reverse convex program ; Rank-two quasiconcave function ; Parametric simplex algorithm
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    Notes: Abstract In this paper, we propose an algorithm for solving a linear program with an additional rank-two reverse convex constraint. Unlike the existing methods which generate approximately optimal solutions, the algorithm provides a rigorous optimal solution to this nonconvex problem by a finite number of dual pivot operations. Computational results indicate that the algorithm is practical and can solve fairly large scale problems.
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    Journal of global optimization 13 (1998), S. 61-74 
    ISSN: 1573-2916
    Keywords: Global optimization ; Global optimality conditions ; Global search algorithm
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    Notes: Abstract This paper is devoted to solving a reverse-convex problem. The approach presented here is based on Global Optimality Conditions. We propose a general conception of a Global Search Algorithm and develop each part of it. The results of numerical experiments with the dimension up to 400 are also given.
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    Keywords: Efficient set ; Global optimization ; Multiple objective linear programming ; Outer approximation ; Vector maximization
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    Notes: Abstract Various difficulties have been encountered in using decision set-based vector maximization methods to solve a multiple objective linear programming problem (MOLP). Motivated by these difficulties, some researchers in recent years have suggested that outcome set-based approaches should instead be developed and used to solve problem (MOLP). In this article, we present a finite algorithm, called the Outer Approximation Algorithm, for generating the set of all efficient extreme points in the outcome set of problem (MOLP). To our knowledge, the Outer Approximation Algorithm is the first algorithm capable of generating this set. As a by-product, the algorithm also generates the weakly efficient outcome set of problem (MOLP). Because it works in the outcome set rather than in the decision set of problem (MOLP), the Outer Approximation Algorithm has several advantages over decision set-based algorithms. It is also relatively easy to implement. Preliminary computational results for a set of randomly-generated problems are reported. These results tangibly demonstrate the usefulness of using the outcome set approach of the Outer Approximation Algorithm instead of a decision set-based approach.
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    Journal of global optimization 13 (1998), S. 255-267 
    ISSN: 1573-2916
    Keywords: Global optimization ; Topography graph ; Crystal growth
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    Notes: Abstract A new approach for topographical global minimization of a function f(x), x ∈ A ⊂ Rn by using sampled points in A is presented. The globally sampled points are firstly obtained by uniform random sampling or uniform sampling with threshold distances. The point with the lowest function value is used as the nucleus atom to start a crystal growth process. A first triangular nucleus includes the nucleus atom and two nearest points. Sequential crystal growth is continued for which a point next closest to the nucleus atom is bonded to the crystal by attaching to two nearest solidified points. A solidified point will be marked during the crystal growth process if any of two connected points has a lower function value. Upon completion of entire crystal growth process, all unmarked points are then used as starting points for subsequent local minimizations. Extension of the topographical algorithms to constrained problems is exercised by using penalty functions. Formulas for estimation on the number of sampled points for problems with an assumed number of local minima are provided. Results on three global minimization problems by two topographical algorithms are discussed.
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    Journal of global optimization 13 (1998), S. 225-240 
    ISSN: 1573-2916
    Keywords: Low rank concave quadratic programming problem ; Tuy's cutting plane ; Rosen's cutting plane ; Global optimization ; Tabu-search ; Heuristic algorithm
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    Notes: Abstract In this paper, we will propose an efficient heuristic algorithm for solving concave quadratic programming problems whose rank of the objective function is relatively small. This algorithm is a combination of Tuy's cutting plane to eliminate the feasible region and a kind of tabu-search method to find a ‘good’ vertex. We first generate a set of V of vertices and select one of these vertices as a starting point at each step, and apply tabu-search and Tuy's cutting plane algorithm where the list of tabu consists of those vertices eliminated by cutting planes and those newly generated vertices by cutting planes. When all vertices of the set V are eliminated, the algorithm is terminated. This algorithm need not converge to a global minimum, but it can work very well when the rank is relatively small (up to seven). The incumbent solutions are in fact globally optimal for all tested problems. We also propose an alternative algorithm by incorporating Rosen's hyperrectangle cut. This algorithm is more efficient than the combination of Tuy's cutting plane and tabu-search.
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    Journal of global optimization 13 (1998), S. 417-432 
    ISSN: 1573-2916
    Keywords: Branch-and-bound ; Global optimization ; Indefinite quadratic optimization under indefinite quadratic constraints
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    Notes: Abstract In this paper we present an algorithm for solving nonconvex quadratically constrained quadratic programs (all-quadratic programs). The method is based on a simplicial branch-and-bound scheme involving mainly linear programming subproblems. Under the assumption that a feasible point of the all-quadratic program is known, the algorithm guarantees an ε-approximate optimal solution in a finite number of iterations. Computational experiments with an implementation of the procedure are reported on randomly generated test problems. The presented algorithm often outperforms a comparable rectangular branch-and-bound method.
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    Journal of optimization theory and applications 98 (1998), S. 431-448 
    ISSN: 1573-2878
    Keywords: Global optimization ; partitioned random search ; sequential sampling ; dynamic programming
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    Notes: Abstract The paper studies the optimal sequential sampling policy of the partitioned random search (PRS) and its approximation. The PRS is a recently proposed approach for function optimization. It takes explicitly into consideration computation time or cost, assuming that there exist both a cost for each function evaluation and a finite total computation time constraint. It is also motivated at improving efficiency of the widely used crude random search. In particular, the PRS considers partitioning the search region of an objective function into K subregions and employing an independent and identically distributed random sampling scheme for each of K subregions. A sampling policy decides when to terminate the sampling process or which subregion to be sampled next.
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    Journal of global optimization 12 (1998), S. 383-404 
    ISSN: 1573-2916
    Keywords: Factorable functions ; Global optimization ; Linear bounding functions
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    Notes: Abstract Recently linear bounding functions (LBFs) were proposed and used to find ε-global minima. This paper presents an LBF-based algorithm for multivariate global optimization problems. The algorithm consists of three phases. In the global phase, big subregions not containing a solution are quickly eliminated and those which possibly contain the solution are detected. An efficient scheme for the local phase is developed using our previous local minimization algorithm, which is globally convergent with superlinear/quadratic rate and does not require evaluation of gradients and Hessian matrices. To ensure that the found minimizers are indeed the global solutions or save computation effort, a third phase called the verification phase is often needed. Under adequate conditions the algorithm finds the ε-global solution(s) within finite steps. Numerical testing results illustrate how the algorithm works, and demonstrate its potential and feasibility.
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    Journal of global optimization 10 (1997), S. 77-90 
    ISSN: 1573-2916
    Keywords: Global optimization ; parametric quadratic programming ; non-convex quadratic program.
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    Notes: Abstract We present a method which when applied to certain non-convex QP will locatethe globalminimum, all isolated local minima and some of the non-isolated localminima. The method proceeds by formulating a (multi) parametric convex QP interms ofthe data of the given non-convex QP. Based on the solution of the parametricQP,an unconstrained minimization problem is formulated. This problem ispiece-wisequadratic. A key result is that the isolated local minimizers (including theglobalminimizer) of the original non-convex problem are in one-to-one correspondencewiththose of the derived unconstrained problem. The theory is illustrated with several numerical examples. A numericalprocedure isdeveloped for a special class of non-convex QP's. It is applied to a problemfrom theliterature and verifies a known global optimum and in addition, locates apreviously unknown local minimum.
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    Journal of optimization theory and applications 95 (1997), S. 347-369 
    ISSN: 1573-2878
    Keywords: Global optimization ; duality gap ; branch-and-bound techniques ; partly convex programs ; bilinear constraints ; sum of ratios ; reverse convex constraints
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    Notes: Abstract It is shown that, for very general classes of nonconvex global optimization problems, the duality gap obtained by solving a corresponding Lagrangian dual in reduced to zero in the limit when combined with suitably refined partitioning of the feasible set. A similar result holds for partly convex problems where exhaustive partitioning is applied only in the space of nonconvex variables. Applications include branch-and-bound approaches for linearly constrained problems where convex envelopes can be computed, certain generalized bilinear problems, linearly constrained optimization of the sum of ratios of affine functions, and concave minimization under reverse convex constraints.
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    Journal of global optimization 10 (1997), S. 257-281 
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    Keywords: Global optimization ; multiextremal algorithms ; Lipschitzian first derivatives ; convergence ; numerical experiments.
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    Notes: Abstract In this paper we propose a new multi-dimensional methodto solve unconstrained global optimization problems with Lipschitzianfirst derivatives. The method is based on apartition scheme that subdivides the search domain into a set of hypercubesin the course of optimization. This partitioning is regulated by thedecision rule that provides evaluation of the "importance"of each generated hypercube and selection of some partition element to performthe next iteration. Sufficient conditions of global convergence for the newmethod are investigated. Results of numerical experiments are alsopresented.
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    Journal of global optimization 11 (1997), S. 181-191 
    ISSN: 1573-2916
    Keywords: Global optimization ; continuous variables ; aspiration value ; simulated annealing ; stochastic
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    Notes: Abstract An aspiration based simulated annealing algorithm for continuousvariables has been proposed. The new algorithm is similar to the one givenby Dekkers and Aarts (1991) except that a kind of memory is introduced intothe procedure with a self-regulatory mechanism. The algorithm has beenapplied to a set of standard global optimization problems and a number ofmore difficult, complex, practical problems and its performance comparedwith that of the algorithm of Dekkers and Aarts (1991). The new algorithmappears to offer a useful alternative to some of the currently availablestochastic algorithms for global optimization.
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    Journal of global optimization 11 (1997), S. 287-311 
    ISSN: 1573-2916
    Keywords: Global optimization ; branch and bound ; reduced space ; convexenvelope
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    Notes: Abstract A general class of branch and bound algorithms forsolving a wide class of nonlinear programs with branching only in asubset of the problem variables is presented. By reducing the dimension of thesearch space, this technique may dramatically reduce the number ofiterations and time required for convergence to ∈ tolerancewhile retaining proven exact convergence in the infinite limit. Thispresentation includes specifications of the class of nonlinearprograms, a statement of a class of branch and bound algorithms, aconvergence proof, and motivating examples with results.
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    Journal of global optimization 11 (1997), S. 377-385 
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    Keywords: Global optimization ; β-distribution ; controlled random search
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    Notes: Abstract In this paper we propose a new version of the Controlled Random Search(CRS) algorithm of Price. The new algorithmhas been tested on thirteen global optimization test problems. Numericalexperiments indicate that the resulting algorithm performs considerablybetter than the earlier versions of the CRS algorithms. The algorithm,therefore, could offer a reasonable alternative to many currently availablestochastic algorithms, especially for problems requiring ’direct search‘type methods. Also a classification of the CRS algorithms is made based on’global technique‘ – ’local technique‘ and the relative performance ofclasses is numerically explored.
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    Journal of global optimization 11 (1997), S. 361-376 
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    Keywords: Global optimization ; curve fitting ; Chebyshev approximation
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    Notes: Abstract We consider the following problem. Given a finite set of pointsy j in $$\mathbb{R}^n $$ we want to determine a hyperplane H such that the maximum Euclidean distance betweenH and the pointsy j is minimized. This problem(CHOP) is a non-convex optimization problem with a special structure. Forexample, all local minima can be shown to be strongly unique. We present agenericity analysis of the problem. Two different global optimizationapproaches are considered for solving (CHOP). The first is a Lipschitzoptimization method; the other a cutting plane method for concaveoptimization. The local structure of the problem is elucidated by analysingthe relation between (CHOP) and certain associated linear optimizationproblems. We report on numerical experiments.
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    Journal of optimization theory and applications 95 (1997), S. 545-563 
    ISSN: 1573-2878
    Keywords: Global optimization ; real life problems ; pig liver likelihood function ; many-body potential function ; tank reactor ; optimal control
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    Notes: Abstract We describe global optimization problems from three different fields representing many-body potentials in physical chemistry, optimal control of a chemical reactor, and fitting a statistical model to empirical data. Historical background for each of the problems as well as the practical significance of the first two are given. The problems are solved by using eight recently developed stochastic global optimization algorithms representing controlled random search (4 algorithms), simulated annealing (2 algorithms), and clustering (2 algorithms). The results are discussed, and the importance of global optimization in each respective field is focused.
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    Journal of global optimization 11 (1997), S. 163-180 
    ISSN: 1573-2916
    Keywords: Global optimization ; production-transportation problem ; minimum concave-cost flow problem ; primal-dual algorithm ; pseudo-polynomial algorithm
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    Notes: Abstract In this paper, we propose a primal-dual algorithm for solving a class ofproduction-transportation problems. Among m(≥ 2) sources two factoriesexist, which produce given goods at some concave cost and supply them to nterminals. We show that one can globally minimize the total cost ofproduction and transportation by solving a Hitchcock transportation problemwith m sources and n terminals and a minimum linear-cost flow problem withm+n nodes. The number of arithmetic operations required by the algorithm ispseudo-polynomial in the problem input length.
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    Applied mathematics & optimization 34 (1996), S. 161-182 
    ISSN: 1432-0606
    Keywords: Global optimization ; Quadratic cost ; Quadratic equality constraints ; Multivariate polynomials ; Resultants ; 49N99 ; 90C26 ; 90C30 ; 65K05
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    Notes: Abstract In the area of broad-band antenna array signal processing, the global minimum of a quadratic equality constrained quadratic cost minimization problem is often required. The problem posed is usually characterized by a large optimization space (around 50–90 tuples), a large number of linear equality constraints, and a few quadratic equality constraints each having very low rank quadratic constraint matrices. Two main difficulties arise in this class of problem. Firstly, the feasibility region is nonconvex and multiple local minima abound. This makes conventional numerical search techniques unattractive as they are unable to locate the global optimum consistently (unless a finite search area is specified). Secondly, the large optimization space makes the use of decision-method algorithms for the theory of the reals unattractive. This is because these algorithms involve the solution of the roots of univariate polynomials of order to the square of the optimization space. In this paper we present a new algorithm which exploits the structure of the constraints to reduce the optimization space to a more manageable size. The new algorithm relies on linear-algebra concepts, basic optimization theory, and a multivariate polynomial root-solving tool often used by decision-method algorithms.
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    Journal of classification 13 (1996), S. 3-18 
    ISSN: 1432-1343
    Keywords: Unidimensional scaling ; Seriation ; Local minima ; Global optimization ; Smoothing technique ; Multidimensional scaling
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    Notes: Abstract For the problem of metric unidimensional scaling, the number of local minima is estimated. For locating the globally optimal solution we develop an approach, called the “smoothing technique.” Although not guaranteed inevitably to locate the global optimum, the smoothing technique did so in all computational experiments where the global optimum was known.
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    Journal of optimization theory and applications 88 (1996), S. 561-583 
    ISSN: 1573-2878
    Keywords: Global optimization ; biconcave programming ; concave minimization ; bilinear and quadratic programming ; branch-and-bound algorithms ; outer approximations.
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    Notes: Abstract A decomposition approach is proposed for minimizing biconcave functions over polytopes. Important special cases include concave minimization, bilinear and indefinite quadratic programming for which new algorithms result. The approach introduces a new polyhedral partition and combines branch-and-bound techniques, outer approximation, and projection of polytopes in a suitable way.
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    Journal of optimization theory and applications 90 (1996), S. 417-434 
    ISSN: 1573-2878
    Keywords: Global optimization ; primal-relaxed dual approach ; penalty methods ; nonsmooth optimization
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    Notes: Abstract A generalized primal-relaxed dual algorithm for global optimization is proposed and its convergence is proved. The (GOP) algorithm of Floudas and Visweswaran (Refs. 1–2) is shown to be a special case of this general algorithm. Within the proposed framework, the algorithm of Floudas and Visweswaran (Refs. 1–2) is further extended to the nonsmooth case. A penalty implementation of the extended (GOP) algorithm is studied to improve its efficiency.
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    Journal of global optimization 9 (1996), S. 1-22 
    ISSN: 1573-2916
    Keywords: Global optimization ; parallel computing ; interval arithmetic ; branch and bound ; dynamic load balancing
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    Notes: Abstract We present a new parallel method for verified global optimization, using a centralized mediator for the dynamic load balancing. The new approach combines the advantages of two previous models, the master slave model and the processor farm. Numerical results show the efficiency of this new method. For a large number of problems at least linear speedup is reached. The efficiency of this new method is also confirmed by a comparison with other parallel methods for verified global optimization. A theoretical study proves that using the best-first strategy to choose the next box for subdivision, no real superlinear speedup may be expected concerning the number of iterations. Moreover, the potential of parallelization of methods of verified global optimization is discussed in general.
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    Journal of global optimization 9 (1996), S. 141-151 
    ISSN: 1573-2916
    Keywords: Global optimization ; optimality condition ; second-order sufficient condition ; verification of convexity ; interval analysis ; primary 90C30 ; secondary 65G10
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    Notes: Abstract This paper presents a new approach to the sufficient conditions of nonlinear programming. Main result is a sufficient condition for the global optimality of a Kuhn-Tucker point. This condition can be verified constructively, using a novel convexity test based on interval analysis, and is guaranteed to prove global optimality of strong local minimizers for sufficiently narrow bounds. Hence it is expected to be a useful tool within branch and bound algorithms for global optimization.
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    Journal of optimization theory and applications 88 (1996), S. 751-763 
    ISSN: 1573-2878
    Keywords: Global optimization ; functions with concave minorants ; d.c. optimization ; branch-and-bounds methods ; systems of inequalities
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    Notes: Abstract In this note, we show how a recent approach for solving linearly constrained multivariate Lipschitz optimization problems and corresponding systems of inequalities can be generalized to solve optimization problems where the objective function is only assumed to possess a concave minorant at each point. This class of functions includes not only Lipschitz functions and some generalizations, such as certain ρ-convex functions and Hölder functions with exponent greater than one, but also all functions which can be expressed as differences of two convex functions (d.c. functions). Thus, in particular, a new approach is obtained for the important problem of minimizing a d.c. function over a polytope.
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    Journal of global optimization 8 (1996), S. 323-348 
    ISSN: 1573-2916
    Keywords: Global optimization ; Hölder functions ; Lipschitz optimization
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    Notes: Abstract We propose a branch-and-bound framework for the global optimization of unconstrained Hölder functions. The general framework is used to derive two algorithms. The first one is a generalization of Piyavskii's algorithm for univariate Lipschitz functions. The second algorithm, using a piecewise constant upper-bounding function, is designed for multivariate Hölder functions. A proof of convergence is provided for both algorithms. Computational experience is reported on several test functions from the literature.
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    Journal of global optimization 8 (1996), S. 379-391 
    ISSN: 1573-2916
    Keywords: Global optimization ; optimality condition ; convex function ; numerical algorithm ; simple set
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    Topics: Mathematics
    Notes: Abstract The problem of maximizing a convex function on a so-called simple set is considered. Based on the optimality conditions [19], an algorithm for solving the problem is proposed. This numerical algorithm is shown to be convergent. The proposed algorithm has been implemented and tested on a variety of test problems.
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    Journal of global optimization 8 (1996), S. 107-138 
    ISSN: 1573-2916
    Keywords: Global optimization ; range reduction ; branch-and-bound ; polynomial programming ; multiplicative programming ; mixed-integer nonlinear programming ; quadratic programming
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    Topics: Mathematics
    Notes: Abstract This paper presents valid inequalities and range contraction techniques that can be used to reduce the size of the search space of global optimization problems. To demonstrate the algorithmic usefulness of these techniques, we incorporate them within the branch-and-bound framework. This results in a branch-and-reduce global optimization algorithm. A detailed discussion of the algorithm components and theoretical properties are provided. Specialized algorithms for polynomial and multiplicative programs are developed. Extensive computational results are presented for engineering design problems, standard global optimization test problems, univariate polynomial programs, linear multiplicative programs, mixed-integer nonlinear programs and concave quadratic programs. For the problems solved, the computer implementation of the proposed algorithm provides very accurate solutions in modest computational time.
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    Journal of global optimization 8 (1996), S. 379-391 
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    Keywords: Global optimization ; optimality condition ; convex function ; numerical algorithm ; simple set
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    Topics: Mathematics
    Notes: Abstract The problem of maximizing a convex function on a so-called simple set is considered. Based on the optimality conditions [19], an algorithm for solving the problem is proposed. This numerical algorithm is shown to be convergent. The proposed algorithm has been implemented and tested on a variety of test problems.
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    Journal of global optimization 8 (1996), S. 81-90 
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    Keywords: Global optimization ; average performance ; Brownian motion
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    Notes: Abstract In this paper we study random non-adaptive algorithms for finding the maximum of a continuous function on the unit interval. We compare the average performance of different algorithms under the assumption of Wiener measure on the space of continuous functions. Placing the observations independently according to a Beta(2/3,2/3) density function is shown to be the optimal random non-adaptive algorithm. The performance is compared with other random and deterministic non-adaptive algorithms.
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    Journal of global optimization 8 (1996), S. 413-427 
    ISSN: 1573-2916
    Keywords: Global optimization ; random methods ; heuristic solution strategy ; local search ; limited solution time
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    Notes: Abstract In this paper the box constrained global optimization problem in presence of a limited solution time is considered. A method is studied based on a combination of multistart and singlestart which implies a decision sequence on the number of random points to be generated. Search strategies are numerically illustrated. Criteria are introduced to measure the performance of solution methods for the problem class. Moreover, the performance of search strategies, specifically the efficiency of generating random points is analyzed.
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    Journal of global optimization 8 (1996), S. 413-427 
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    Keywords: Global optimization ; random methods ; heuristic solution strategy ; local search ; limited solution time
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    Notes: Abstract In this paper the box constrained global optimization problem in presence of a limited solution time is considered. A method is studied based on a combination of multistart and singlestart which implies a decision sequence on the number of random points to be generated. Search strategies are numerically illustrated. Criteria are introduced to measure the performance of solution methods for the problem class. Moreover, the performance of search strategies, specifically the efficiency of generating random points is analyzed.
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    Journal of global optimization 8 (1996), S. 323-348 
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    Keywords: Global optimization ; Hölder functions ; Lipschitz optimization
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    Notes: Abstract We propose a branch-and-bound framework for the global optimization of unconstrained Hölder functions. The general framework is used to derive two algorithms. The first one is a generalization of Piyavskii's algorithm for univariate Lipschitz functions. The second algorithm, using a piecewise constant upper-bounding function, is designed for multivariate Hölder functions. A proof of convergence is provided for both algorithms. Computational experience is reported on several test functions from the literature.
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    Annals of operations research 58 (1995), S. 261-278 
    ISSN: 1572-9338
    Keywords: Global optimization ; Wiener process ; sequential stopping rules ; one-step look-ahead
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    Topics: Mathematics , Economics
    Notes: Abstract In this paper a new algorithm is proposed, based upon the idea of modeling the objective function of a global optimization problem as a sample path from a Wiener process. Unlike previous work in this field, in the proposed model the parameter of the Wiener process is considered as a random variable whose conditional (posterior) distribution function is updated on-line. Stopping criteria for Bayesian algorithms are discussed and detailed proofs on finite-time stopping are provided.
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    Mathematical programming 69 (1995), S. 443-448 
    ISSN: 1436-4646
    Keywords: Global optimization ; Discrete optimization ; Algorithm complexity ; Random search ; Markov chains
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    Topics: Computer Science , Mathematics
    Notes: Abstract Pure Adaptive Search is a stochastic algorithm which has been analyzed for continuous global optimization. When a uniform distribution is used in PAS, it has been shown to have complexity which is linear in dimension. We define strong and weak variations of PAS in the setting of finite global optimization and prove analogous results. In particular, for then-dimensional lattice {1,⋯,k} n , the expected number of iterations to find the global optimum is linear inn. Many discrete combinatorial optimization problems, although having intractably large domains, have quite small ranges. The strong version of PAS for all problems, and the weak version of PAS for a limited class of problems, has complexity the order of the size of the range.
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    Journal of optimization theory and applications 84 (1995), S. 443-455 
    ISSN: 1573-2878
    Keywords: Global optimization ; nonconvex programming ; mathematical programming ; DC-programming ; branch-and-bound methods
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    Topics: Mathematics
    Notes: Abstract In Ref. 1, a general class of branch-and-bound methods was proposed by Horst for solving global optimization problems. One of the main contributions of Ref. 1 was the opportunity of handling partition elements whose feasibility is not known. Deletion-by-infeasibility rules were presented for problems where the feasible set is convex, is defined by finitely many convex and reverse convex constraints, or is defined by Lipschitzian inequalities. In this note, we propose a new deletion-by-infeasibility rule for problems whose feasible set is defined by functions representable as differences of convex functions.
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    Journal of global optimization 6 (1995), S. 1-37 
    ISSN: 1573-2916
    Keywords: Global optimization ; simulated annealing ; Monte Carlo optimization
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    Notes: Abstract A method is presented for attempting global minimization for a function of continuous variables subject to constraints. The method, calledAdaptive Simulated Annealing (ASA), is distinguished by the fact that the fixed temperature schedules and step generation routines that characterize other implementations are here replaced by heuristic-based methods that effectively eliminate the dependence of the algorithm's overall performance on user-specified control parameters. A parallelprocessing version of ASA that gives increased efficiency is presented and applied to two standard problems for illustration and comparison.
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    Journal of optimization theory and applications 86 (1995), S. 369-388 
    ISSN: 1573-2878
    Keywords: Global optimization ; Lipschitz optimization ; systems of inequalities ; branch-and-bounds methods
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    Notes: Abstract Most numerically promising methods for solving multivariate unconstrained Lipschitz optimization problems of dimension greater than two use rectangular or simplicial branch-and-bound techniques with computationally cheap but rather crude lower bounds. Generalizations to constrained problems, however, require additional devices to detect sufficiently many infeasible partition sets. In this article, a new lower bounding procedure is proposed for simplicial methods yielding considerably better bounds at the expense of two linear programs in each iteration. Moreover, the resulting approach can solve easily linearly constrained problems, since in this case infeasible partition sets are automatically detected by the lower bounding procedure. Finally, it is shown that the lower bounds can be further improved when the method is applied to solve systems of inequalities. Implementation issues, numerical experiments, and comparisons are discussed in some detail.
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    Journal of global optimization 6 (1995), S. 269-292 
    ISSN: 1573-2916
    Keywords: Global optimization ; inventory problems ; discrete Hamilton-Jacobi-Bellman equations ; quasi-variational inequalities ; subsolutions and supersolutions
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    Notes: Abstract We consider the numerical resolution of hierarchical inventory problems under global optimization. First we describe the model as well as the dynamical stochastic system and the impulse controls involved. Next we characterize the optimal cost function and we formulate the Hamilton-Jacobi-Bellman equations. We present a numerical scheme and a fast algorithm of resolution, with a result on the speed of convergence. Finally, we apply the discretization method to some examples where we show the usefulness of the proposed numerical method as well as the advantages of operating under global optimization.
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    Journal of global optimization 7 (1995), S. 297-331 
    ISSN: 1573-2916
    Keywords: 49D37 ; 65G10 ; Global optimization ; interval arithmetic
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    Topics: Mathematics
    Notes: Abstract In this paper, we give a new branch and bound algorithm for the global optimization problem with bound constraints. The algorithm is based on the use of inclusion functions. The bounds calculated for the global minimum value are proved to be correct, all rounding errors are rigorously estimated. Our scheme attempts to exclude most “uninteresting” parts of the search domain and concentrates on its “promising” subsets. This is done as fast as possible (by involving local descent methods), and uses little information as possible (no derivatives are required). Numerical results for many well-known problems as well as some comparisons with other methods are given.
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    Journal of global optimization 6 (1995), S. 293-311 
    ISSN: 1573-2916
    Keywords: Global optimization ; univariate optimization ; polynomials ; rational functions ; Storm's chain
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    Notes: Abstract Sturm's chain technique for evaluation of a number of real roots of polynomials is applied to construct a simple algorithm for global optimization of polynomials or generally for rational functions of finite global minimal value. The method can be applied both to find the global minimum in an interval or without any constraints. It is shown how to use the method to minimize globally a truncated Fourier series. The results of numerical tests are presented and discussed. The cost of the method scales as the square of the degree of the polynomial.
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    Journal of global optimization 7 (1995), S. 183-207 
    ISSN: 1573-2916
    Keywords: Global optimization ; interval arithmetic ; branch-and-bound ; interval subdivision
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    Notes: Abstract This paper investigates the influence of the interval subdivision selection rule on the convergence of interval branch-and-bound algorithms for global optimization. For the class of rules that allows convergence, we study the effects of the rules on a model algorithm with special list ordering. Four different rules are investigated in theory and in practice. A wide spectrum of test problems is used for numerical tests indicating that there are substantial differences between the rules with respect to the required CPU time, the number of function and derivative evaluations, and the necessary storage space. Two rules can provide considerable improvements in efficiency for our model algorithm.
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    Journal of global optimization 7 (1995), S. 407-419 
    ISSN: 1573-2916
    Keywords: Global optimization ; nonlinear constraints ; local tuning ; index scheme ; global convergence
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    Notes: Abstract In this paper we propose an algorithm using only the values of the objective function and constraints for solving one-dimensional global optimization problems where both the objective function and constraints are Lipschitzean and nonlinear. The constrained problem is reduced to an unconstrained one by the index scheme. To solve the reduced problem a new method with local tuning on the behavior of the objective function and constraints over different sectors of the search region is proposed. Sufficient conditions of global convergence are established. We also present results of some numerical experiments.
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    Journal of global optimization 7 (1995), S. 143-182 
    ISSN: 1573-2916
    Keywords: Global optimization ; nonlinear systems of equations ; all solutions
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    Notes: Abstract A new approach is proposed for finding allε-feasible solutions for certain classes of nonlinearly constrained systems of equations. By introducing slack variables, the initial problem is transformed into a global optimization problem (P) whose multiple global minimum solutions with a zero objective value (if any) correspond to all solutions of the initial constrained system of equalities. Allε-globally optimal points of (P) are then localized within a set of arbitrarily small disjoint rectangles. This is based on a branch and bound type global optimization algorithm which attains finiteε-convergence to each of the multiple global minima of (P) through the successive refinement of a convex relaxation of the feasible region and the subsequent solution of a series of nonlinear convex optimization problems. Based on the form of the participating functions, a number of techniques for constructing this convex relaxation are proposed. By taking advantage of the properties of products of univariate functions, customized convex lower bounding functions are introduced for a large number of expressions that are or can be transformed into products of univariate functions. Alternative convex relaxation procedures involve either the difference of two convex functions employed in αBB [23] or the exponential variable transformation based underestimators employed for generalized geometric programming problems [24]. The proposed approach is illustrated with several test problems. For some of these problems additional solutions are identified that existing methods failed to locate.
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    Journal of global optimization 7 (1995), S. 337-363 
    ISSN: 1573-2916
    Keywords: Global optimization ; constrained optimization ; convex relaxation
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    Topics: Mathematics
    Notes: Abstract A branch and bound global optimization method,αBB, for general continuous optimization problems involving nonconvexities in the objective function and/or constraints is presented. The nonconvexities are categorized as being either of special structure or generic. A convex relaxation of the original nonconvex problem is obtained by (i) replacing all nonconvex terms of special structure (i.e. bilinear, fractional, signomial) with customized tight convex lower bounding functions and (ii) by utilizing the α parameter as defined in [17] to underestimate nonconvex terms of generic structure. The proposed branch and bound type algorithm attains finiteε-convergence to the global minimum through the successive subdivision of the original region and the subsequent solution of a series of nonlinear convex minimization problems. The global optimization method,αBB, is implemented in C and tested on a variety of example problems.
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    Mathematical programming 63 (1994), S. 193-212 
    ISSN: 1436-4646
    Keywords: Global optimization ; nonconvex programming ; duality gap ; branch and bound method ; decomposition ; nonsmooth optimization ; pooling problem
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    Topics: Computer Science , Mathematics
    Notes: Abstract We derive a general principle demonstrating that by partitioning the feasible set, the duality gap, existing between a nonconvex program and its lagrangian dual, can be reduced, and in important special cases, even eliminated. The principle can be implemented in a Branch and Bound algorithm which computes an approximate global solution and a corresponding lower bound on the global optimal value. The algorithm involves decomposition and a nonsmooth local search. Numerical results for applying the algorithm to the pooling problem in oil refineries are given.
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    Journal of optimization theory and applications 80 (1994), S. 441-464 
    ISSN: 1573-2878
    Keywords: Global optimization ; univariate functions ; interval arithmetic ; Taylor's formula
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    Topics: Mathematics
    Notes: Abstract Interval arithmetic and Taylor's formula can be used to bound the slope of the cord of a univariate function at a given point. This leads in turn to bounding the values of the function itself. Computing such bounds for the function, its first and second derviatives, allows the determination of intervals in which this function cannot have a global minimum. Exploiting this information together with a simple branching rule yields an efficient algorithm for global minimization of univariate functions. Computational experience is reported.
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    Journal of optimization theory and applications 80 (1994), S. 513-536 
    ISSN: 1573-2878
    Keywords: Global optimization ; parallel numerical methods ; convergence ; speedup
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    Topics: Mathematics
    Notes: Abstract We describe a new parallel method for solving global optimization problems. The formulation of the decision rules of this method is presented. We examine convergence conditions of the proposed algorithm and establish conditions which guarantee a considerable speedup with respect to the sequential version of the algorithm. We also present some numerical experiments executed on Alliant FX/80 for one class of multiextremal functions.
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    Journal of global optimization 4 (1994), S. 17-35 
    ISSN: 1573-2916
    Keywords: Global optimization ; Multistart algorithm ; sequential stopping rules ; nonparametric models ; clustering techniques ; censored observations
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    Topics: Mathematics
    Notes: Abstract In this paper a new algorithm is proposed for global optimization problems. The main idea is that of modifying a standard clustering approach by sequentially sampling the objective function while adaptively deciding an appropriate sample size. Theoretical as well as computational results are presented.
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    Journal of global optimization 5 (1994), S. 349-358 
    ISSN: 1573-2916
    Keywords: Global optimization ; multilevel single linkage ; topographs ; graph minima
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    Topics: Mathematics
    Notes: Abstract An iterative topographical Multilevel Single Linkage (TMSL) method has been introduced. The approach uses topographical information on the objective function, in particular theg-nearest-neighbour graph. The algorithm uses evenly distributed points from a Halten sequence of uniform limiting density. We discuss the implementation of the algorithm and compare its performance with other well-known algorithms. The new algorithm performs much better (in some cases several times) than the Multilevel Single Linkage method in terms of number of function evaluations but is not quite so competitive with respect to CPU time.
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    Journal of global optimization 5 (1994), S. 359-370 
    ISSN: 1573-2916
    Keywords: Global optimization ; necessary and sufficient optimality conditions ; Mathematics Subject Classifications (1991) ; 49K27 ; 90C26 ; 90C48
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    Topics: Mathematics
    Notes: Abstract By means of suitable dual problems to the following global optimization problems: extremum{f(x): x εM ⊂X}, wheref is a proper convex and lower-semicontinuous function andM a nonempty, arbitrary subset of a reflexive Banach spaceX, we derive necessary and sufficient optimality conditions for a global minimizer. The method is also applicable to other nonconvex problems and leads to at least necessary global optimality conditions.
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    Journal of global optimization 5 (1994), S. 373-397 
    ISSN: 1573-2916
    Keywords: Global optimization ; decomposition ; maximum likelihood estimation ; Weibull distribution
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    Topics: Mathematics
    Notes: Abstract Much work has been devoted to the problem of finding maximum likelihood estimators for the three-parameter Weibull distribution. This problem has not been clearly recognized as a global optimization one and most methods from the literature occasionally fail to find a global optimum. We develop a global optimization algorithm which uses first order conditions and projection to reduce the problem to a univariate optimization one. Bounds on the resulting function and its first order derivative are obtained and used in a branch-and-bound scheme. Computational experience is reported. It is also shown that the solution method we propose can be extended to the case of right censored samples.
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    Journal of global optimization 5 (1994), S. 195-199 
    ISSN: 1573-2916
    Keywords: Global optimization ; test functions ; multidimensional scaling
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    Notes: Abstract We suggest weighted least squares scaling, a basic method in multidimensional scaling, as a class of test functions for global optimization. The functions are easy to code, cheap to calculate, and have important applications in data analysis. For certain data these functions have many local minima. Some characteristic features of the test functions are investigated.
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    Journal of optimization theory and applications 82 (1994), S. 267-293 
    ISSN: 1573-2878
    Keywords: Global optimization ; convex-concave programming ; branch-and-bound methods ; optimization of differences of convex functions ; fractional multiplicative programming
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    Notes: Abstract A class of branch-and-bound methods is proposed for minimizing a quasiconvex-concave function subject to convex and quasiconvex-concave inequality constraints. Several important special cases where the subproblems involved by the bounding-and-branching operations can be solved quite effectively include certain d.c. programming problems, indefinite quadratic programming with one negative eigenvalue, affine multiplicative problems, and fractional multiplicative optimization.
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    Journal of global optimization 5 (1994), S. 325-332 
    ISSN: 1573-2916
    Keywords: Global optimization ; stochastic methods ; deterministic methods
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    Notes: Abstract For constrained concave global minimization problems, two very different solution techniques have been investigated. The first such method is a stochastic mulitstart approach which typically finds, with high probability, all local minima for the problem. The second method is deterministic and guarantees a global minimum solution to within any user specified tolerance. It is the purpose of this paper to make a careful comparison of these two methods on a range of test problems using separable concave objectives over compact polyhedral sets, and to investigate in this way the advantages and disadvantages of each method. A direct computational comparison, on the same set of over 140 problems, is presented.
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    Journal of global optimization 5 (1994), S. 371-372 
    ISSN: 1573-2916
    Keywords: Global optimization ; quasiconcavity ; reverse-convex contraints
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    Topics: Mathematics
    Notes: Abstract A counterexample to an algorithm of Dien (1988) for solving a minimization problem with a quasiconcave objective function and both linear and a reverse-convex constraint shows that this algorithm needn't lead to a solution of the given problem.
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    Journal of global optimization 4 (1994), S. 329-341 
    ISSN: 1573-2916
    Keywords: Global optimization ; covering methods ; deterministic ; mathematical programming ; 90C30 ; 65K05
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    Notes: Abstract Two improvements for the algorithm of Breiman and Cutler are presented. Better envelopes can be built up using positive quadratic forms. Better utilization of first and second derivative information is attained by combining both global aspects of curvature and local aspects near the global optimum. The basis of the results is the geometric viewpoint developed by the first author and can be applied to a number of covering type methods. Improvements in convergence rates are demonstrated empirically on standard test functions.
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    Journal of global optimization 5 (1994), S. 159-180 
    ISSN: 1573-2916
    Keywords: Global optimization ; random perturbations ; Monte Carlo methods
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    Topics: Mathematics
    Notes: Abstract The paper deals with the global minimization of a differentiable cost function mapping a ball of a finite dimensional Euclidean space into an interval of real numbers. It is established that a suitable random perturbation of the gradient method with a fixed parameter generates a bounded minimizing sequence and leads to a global minimum: the perturbation avoids convergence to local minima. The stated results suggest an algorithm for the numerical approximation of global minima: experiments are performed for the problem of fitting a sum of exponentials to discrete data and to a nonlinear system involving about 5000 variables. The effect of the random perturbation is examined by comparison with the purely deterministic gradient method.
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    Journal of global optimization 5 (1994), S. 267-276 
    ISSN: 1573-2916
    Keywords: Global optimization ; topography graph ; parallel algorithms
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    Notes: Abstract A method for global minimization of a functionf(x), x εA ⊂R n by using presampled global points inA is presented. The global points are obtained by uniform sampling, discarding points too near an already accepted point to obtain a very uniform covering. The accepted points and their nearest-neighbours matrix are stored on a file. When optimzing a given function these pre-sampled points and the matrix are read from file. Then the function value of each point is computed and itsk nearest neighbours that have larger function values are marked. The points for which all its neighbours are marked are extracted as promising starting points for local minimizations. Results from a parallel implementation are presented. The working of a sequential version in Fortran is illustrated.
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    Journal of global optimization 5 (1994), S. 333-348 
    ISSN: 1573-2916
    Keywords: Global optimization ; decomposition ; canonical d.c. program ; conical branch and bound algorithms ; outer approximation ; cutting plane algorithms
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Many global optimization problems can be formulated in the form min{c(x, y): x εX, y εY, (x, y) εZ, y εG} where X, Y are polytopes in ℝ p , ℝ n , respectively, Z is a closed convex set in ℝp+n, while G is the complement of an open convex set in ℝ n . The function c:ℝ p+n → ℝ is assumed to be linear. Using the fact that the nonconvex constraints depend only upon they-variables, we modify and combine basic global optimization techniques such that some new decomposition methods result which involve global optimization procedures only in ℝ n . Computational experiments show that the resulting algorithms work well for problems with smalln.
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    Journal of global optimization 5 (1994), S. 205-251 
    ISSN: 1573-2916
    Keywords: Global optimization ; phase equilibrium ; biconvex and DC programming problems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract An increasingly popular approach when solving the phase and chemical equilibrium problem is to pose it as an optimization problem. However, difficulties are encountered due to the highly nonlinear nature of the models used to represent the behavior of the fluids, and because of the existence of multiple local solutions. This work shows how it is possible to guarantee ε-global solutions for a certain important class of the phase and chemical equilibrium problem, namely when the liquid phase can be modeled using neither the Non-Random Two-Liquid (NRTL) equation, or the UNIversal QUAsi Chemical (UNIQUAC) equation. Ideal vapor phases are easily incorporated into the global optimization framework. A numberof interesting properties are described which drastically alter the structure of the respective problems. For the NRTL equation, it is shown that the formulation can be converted into a biconvex optimization problem. The GOP algorithm of Floudas and Visweswaran [8, 9] can then be used to obtain ε-global solutions in this case. For the UNIQUAC equation, the new properties show how the objective function can be transformed into the difference of two convex functions (i.e. a D.C. programming problem is obtained), where the concave portion is separable. A branch and bound algorithm based on that of Falk and Soland [6] is used to guarantee convergence to an ε-global solution. Examples are presented which demonstrate the performance of both algorithms.
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  • 96
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    Annals of operations research 43 (1993), S. 87-107 
    ISSN: 1572-9338
    Keywords: Global optimization ; parallel computing ; minimum-concave cost network flow problems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract In this paper, we are concerned with the development of parallel algorithms for solving some classes of nonconvex optimization problems. We present an introductory survey of parallel algorithms that have been used to solve structured problems (partially separable, and large-scale block structured problems), and algorithms based on parallel local searches for solving general nonconvex problems. Indefinite quadratic programming posynomial optimization, and the general global concave minimization problem can be solved using these approaches. In addition, for the minimum concave cost network flow problem, we are going to present new parallel search algorithms for large-scale problems. Computational results of an efficient implementation on a multi-transputer system will be presented.
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  • 97
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    Mathematical programming 61 (1993), S. 75-87 
    ISSN: 1436-4646
    Keywords: Global optimization ; branch-and-bound ; convex—concave constraint ; d.c. programming ; the product of two affine functions ; decomposition method ; algorithm
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract An implementable decomposition method based on branch-and-bound techniques is proposed for finding a global optimal solution of certain convex programs with an additional convex—concave constrainth(x, y) ⩽ 0. A nonadaptive simplicial and an adaptive bisection are used for the branching operation, which is performed iny-space only. The bounding operation is based on a relaxation of the convex—concave constrainth(x, y) ⩽ 0. The algorithm can be applied efficiently for linear programs with an additional affine multiplicative constraint.
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  • 98
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    Acta applicandae mathematicae 33 (1993), S. 109-118 
    ISSN: 1572-9036
    Keywords: 60F05 ; 60F17 ; 62L20 ; Global optimization ; multidimensional scaling ; evolutionary strategies ; genetic algorithms
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract We consider multidimensional scaling for embedding dimension two, which allows the detection of structures in dissimilarity data by simply drawing two-dimensional figures. The corresponding objective function, called STRESS, is generally nondifferentiable and has many local minima. In this paper we investigate several features of this function, and discuss the application of different global optimization procedures. A method based on combining a local search algorithm with an evolutionary strategy of generating new initial points is proposed, and its efficiency is investigated by numerical examples.
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  • 99
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    Acta applicandae mathematicae 33 (1993), S. 69-88 
    ISSN: 1572-9036
    Keywords: 60F05 ; 60F17 ; 62L20 ; Global optimization ; global random search ; Statistical inference ; branch and bound ; endpoint of a distribution ; stratified sampling
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract The multivariate multiextremal optimization problem is considered. Various statistical procedures based on the use of the asymptotic theory of extreme order statistics are thoroughly described. These procedures are used to infer about the maximal value of a function by its values at random points. A class of global random search methods underlying the procedures is considered. These methods generalize the well-known branch and bound methods. The article is mainly of a survey nature. It also contains new results.
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  • 100
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    Journal of optimization theory and applications 77 (1993), S. 97-126 
    ISSN: 1573-2878
    Keywords: Global optimization ; dynamical systems ; terminal repellers ; subenergy tunneling function ; artificial neural networks
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract A new method for unconstrained global function optimization, acronymedtrust, is introduced. This method formulates optimization as the solution of a deterministic dynamical system incorporating terminal repellers and a novel subenergy tunneling function. Benchmark tests comparing this method to other global optimization procedures are presented, and thetrust algorithm is shown to be substantially faster. Thetrust formulation leads to a simple stopping criterion. In addition, the structure of the equations enables an implementation of the algorithm in analog VLSI hardware, in the vein of artificial neural networks, for further substantial speed enhancement.
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