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  • 1
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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
    Source: Springer Online Journal Archives 1860-2000
    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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  • 2
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    BIT 26 (1986), S. 392-395 
    ISSN: 1572-9125
    Keywords: 65K05 ; 90C30 ; Fractional programming ; Vertex ranking ; Global optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract In this note we consider an algorithm for quasiconcave nonlinear fractional programming problems, based on ranking the vertices of a linear fractional programming problem and techniques from global optimization.
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  • 3
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    BIT 28 (1988), S. 323-328 
    ISSN: 1572-9125
    Keywords: 90C30 ; 65K05 ; Global optimization ; Quadratic programming ; Convex hull
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract In this paper we consider an algorithm for a class of quadratic problems defined on a polytope which is described as the convex hull of a set of points. The algorithm is based on simplex partitions using convex underestimating functions.
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  • 4
    ISSN: 1436-4646
    Keywords: Global optimization ; nonconvex programming ; branch-and-bound ; restart procedure ; decomposition ; outer approximation ; concave minimization ; d.c. optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract A general branch-and-bound conceptual scheme for global optimization is presented that includes along with previous branch-and-bound approaches also grid-search techniques. The corresponding convergence theory, as well as the question of restart capability for branch-and-bound algorithms used in decomposition or outer approximation schemes are discussed. As an illustration of this conceptual scheme, a finite branch-and-bound algorithm for concave minimization is described and a convergent branch-and-bound algorithm, based on the previous one, is developed for the minimization of a difference of two convex functions.
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  • 5
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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
    Source: Springer Online Journal Archives 1860-2000
    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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  • 6
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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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  • 7
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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
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    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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  • 8
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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
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    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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  • 9
    ISSN: 1573-2878
    Keywords: Global optimization ; nonconvex programming ; mathematical programming ; concave minimization ; DC-programming ; Lipschitzian optimization ; branch-and-bound methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract A crucial problem for many global optimization methods is how to handle partition sets whose feasibility is not known. This problem is solved for broad classes of feasible sets including convex sets, sets defined by finitely many convex and reverse convex constraints, and sets defined by Lipschitzian inequalities. Moreover, a fairly general theory of bounding is presented and applied to concave objective functions, to functions representable as differences of two convex functions, and to Lipschitzian functions. The resulting algorithms allow one to solve any global optimization problem whose objective function is of one of these forms and whose feasible set belongs to one of the above classes. In this way, several new fields of optimization are opened to the application of global methods.
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  • 10
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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
    Source: Springer Online Journal Archives 1860-2000
    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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  • 11
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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.
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    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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  • 12
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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
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    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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  • 13
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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
    Source: Springer Online Journal Archives 1860-2000
    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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  • 14
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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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  • 15
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    Journal of global optimization 6 (1995), S. 1-37 
    ISSN: 1573-2916
    Keywords: Global optimization ; simulated annealing ; Monte Carlo optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    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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  • 16
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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
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    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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  • 17
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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
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    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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  • 18
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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.
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    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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  • 19
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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
    Source: Springer Online Journal Archives 1860-2000
    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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  • 20
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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
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    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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  • 21
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    Journal of global optimization 12 (1998), S. 325-351 
    ISSN: 1573-2916
    Keywords: Chemical and phase equilibrium ; convexity ; Gibbs free energy ; Global optimization ; Non-convex optimization ; Tangent-plane criterion
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    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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  • 22
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    Journal of optimization theory and applications 54 (1987), S. 253-271 
    ISSN: 1573-2878
    Keywords: Global optimization ; multiextremal optimization ; optimization algorithms ; nonlinear programming ; branch-and-bound methods ; Lipschitzian optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract A general class of derivative-free optimization procedures is presented including the corresponding convergence theory. This theory turns out to be very constructive, in the sense that the convergence conditions not only can be verified easily for many existing algorithms, but also allow one to construct new procedures. It is shown that popular methods such as branch-and-bound concepts, Pintér's general class of procedures, the algorithms of Pijavskii, Shubert, and Mladineo, and the approach of Zheng and Galperin can not only be subsumed under this class of methods, but also partly be improved by regarding them within the framework presented.
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  • 23
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    Journal of optimization theory and applications 57 (1988), S. 307-322 
    ISSN: 1573-2878
    Keywords: Global optimization ; nondifferentiable optimization ; Lipschitz continuous functions
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    Topics: Mathematics
    Notes: Abstract An algorithm is presented which locates the global minimum or maximum of a function satisfying a Lipschitz condition. The algorithm uses lower bound functions defined on a partitioned domain to generate a sequence of lower bounds for the global minimum. Convergence is proved, and some numerical results are presented.
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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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    Topics: Mathematics
    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 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 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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    Topics: Mathematics
    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 optimization theory and applications 96 (1998), S. 575-588 
    ISSN: 1573-2878
    Keywords: Global optimization ; convergence ; stochastic algorithms ; deterministic algorithms
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    Topics: Mathematics
    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 14 (1999), S. 157-179 
    ISSN: 1573-2916
    Keywords: Global optimization ; Interval methods ; Local nonsmooth optimization ; Scientific computing
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    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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    Journal of global optimization 14 (1999), S. 205-216 
    ISSN: 1573-2916
    Keywords: Global optimization ; Lipschitz continuity ; Piyavskii's algorithm ; Uniform Continuity
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    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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    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
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    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 
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    Keywords: Global optimization ; Problem features ; Problem classes ; Test problems ; Solution techniques
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    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 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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    Topics: Mathematics
    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 
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    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 
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    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 
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    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 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 
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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. 107-138 
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    Keywords: Global optimization ; range reduction ; branch-and-bound ; polynomial programming ; multiplicative programming ; mixed-integer nonlinear programming ; quadratic programming
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    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 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 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 optimization theory and applications 47 (1985), S. 1-16 
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    Keywords: Global optimization ; stochastic differential equations
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    Notes: Abstract Let ℝ n be then-dimensional real Euclidean space,x=(x 1,x 2, ...,x n)T ∈ ℝ n , and letf:ℝ n → R be a real-valued function. We consider the problem of finding the global minimizers off. A new method to compute numerically the global minimizers by following the paths of a system of stochastic differential equations is proposed. This method is motivated by quantum mechanics. Some numerical experience on a set of test problems is presented. The method compares favorably with other existing methods for global optimization.
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    Journal of optimization theory and applications 48 (1986), S. 303-313 
    ISSN: 1573-2878
    Keywords: Global optimization ; test problems ; concave minimization
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    Notes: Abstract Construction of problems with known global solutions is important for the computational testing of constrained global minimization algorithms. In this paper, it is shown how to construct a concave quadratic function which attains its global minimum at a specified vertex of a polytope inR n+k. The constructed function is strictly concave in the variablesx ∈R n and is linear in the variablesy ∈R k. The number of linear variablesk may be much larger thann, so that large-scale global minimization test problems can be constructed by the methods described here.
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    Journal of optimization theory and applications 49 (1986), S. 367-374 
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    Keywords: Global optimization ; graphical methods
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    Notes: Abstract The construction of Branin trajectories for locating the stationary points of a scalar function of many variables involves, in the general case, the numerical solution of a set of simultaneous ordinary differential equations, or some equivalent numerical procedure. For a function of only two variables which is separable in either the multiplicative or additive sense, it is shown that Branin trajectories may be obtained by a graphical method due to Volterra.
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    Journal of optimization theory and applications 51 (1986), S. 271-291 
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    Keywords: Global optimization ; nonconvex programming ; mathematical programming ; nonlinear optimization ; branch-and-bound methods ; concave minimization
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    Topics: Mathematics
    Notes: Abstract Based on a review of existing algorithms, a general branch-and-bound concept in global optimization is presented. A sufficient and necessary convergence condition is established, and a broad class of realizations is derived that include existing and several new approaches for concave minimization problems.
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    Journal of optimization theory and applications 51 (1986), S. 345-353 
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    Keywords: Global optimization ; stochastic process models ; multidimensional objective functions
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    Notes: Abstract Global optimization requires an adequate internal representation of the objective function for success in a reasonable number of function evaluations. A method for determining the location of a new function evaluation, based on a representation using a stationary stochastic process model, is investigated and some results are given.
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    Journal of optimization theory and applications 61 (1989), S. 111-121 
    ISSN: 1573-2878
    Keywords: Global optimization ; dynamical systems ; search trajectories ; chaos
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    Notes: Abstract We describe a new algorithm which uses the trajectories of a discrete dynamical system to sample the domain of an unconstrained objective function in search of global minima. The algorithm is unusually adept at avoiding nonoptimal local minima and successfully converging to a global minimum. Trajectories generated by the algorithm for objective functions with many local minima exhibit chaotic behavior, in the sense that they are extremely sensitive to changes in initial conditions and system parameters. In this context, chaos seems to have a beneficial effect: failure to converge to a global minimum from a given initial point can often be rectified by making arbitrarily small changes in the system parameters.
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    Journal of optimization theory and applications 61 (1989), S. 143-146 
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    Keywords: Global optimization ; nonconvex programming ; mathematical programming ; branch-and-bound methods
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    Notes: Abstract This technical comment refers to the discussion of strong consistency of several bounding procedures in Lemma 2.1 and Proposition 2.1 of Ref. 1. A necessary clarification is given of the notion of convergence φq → φ in Lemma 2.1, and a derivation of Proposition 2.1 is presented that includes a new and simple consistency proof of the classical bounding by convex envelopes used in many branch-and-bound procedures.
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    Journal of optimization theory and applications 61 (1989), S. 247-270 
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    Keywords: Global optimization ; nondifferentiable optimization ; rational functions ; Lipschitz continuous functions
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    Notes: Abstract A domain partitioning algorithm for minimizing or maximizing a Lipschitz continuous function is enhanced to yield two new, more efficient algorithms. The use of interval arithmetic in the case of rational functions and the estimates of Lipschitz constants valid in subsets of the domain in the case of others and the addition of local optimization have resulted in an algorithm which, in tests on standard functions, performs well.
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    Journal of optimization theory and applications 62 (1989), S. 255-277 
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    Keywords: Global optimization ; acceptance-rejection sampling ; Boltzmann distribution ; random tunneling
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    Topics: Mathematics
    Notes: Abstract Any global minimization algorithm is made by several local searches performed sequentially. In the classical multistart algorithm, the starting point for each new local search is selected at random uniformly in the region of interest. In the tunneling algorithm, such a starting point is required to have the same function value obtained by the last local minimization. We introduce the class of acceptance-rejection based algorithms in order to investigate intermediate procedures. A particular instance is to choose at random the new point approximately according to a Boltzmann distribution, whose temperatureT is updated during the algorithm. AsT → 0, such distribution peaks around the global minima of the cost function, producing a kind of random tunneling effect. The motivation for such an approach comes from recent works on the simulated annealing approach in global optimization. The resulting algorithm has been tested on several examples proposed in the literature.
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    Journal of global optimization 12 (1998), S. 1-36 
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    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 
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    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 
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    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 
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    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 
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    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 7 (1995), S. 407-419 
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    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 8 (1996), S. 379-391 
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    Keywords: Global optimization ; optimality condition ; convex function ; numerical algorithm ; simple set
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    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 7 (1995), S. 143-182 
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    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 
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    Keywords: Global optimization ; constrained optimization ; convex relaxation
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    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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    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 11 (1997), S. 181-191 
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    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 
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    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 
    ISSN: 1573-2916
    Keywords: Global optimization ; β-distribution ; controlled random search
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    Topics: Mathematics
    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 
    ISSN: 1573-2916
    Keywords: Global optimization ; curve fitting ; Chebyshev approximation
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    Topics: Mathematics
    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 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 global optimization 15 (1999), S. 19-39 
    ISSN: 1573-2916
    Keywords: Abstract convexity ; Global optimization ; Increasing function
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    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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    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 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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    Topics: Mathematics
    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 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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    Topics: Mathematics
    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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    Topics: Mathematics
    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 15 (1999), S. 315-342 
    ISSN: 1573-2916
    Keywords: Multiplicative programming ; Convex multiplicative programming ; Global optimization ; Outer approximation ; Branch and bound ; Nonconvex programming
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    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
    Source: Springer Online Journal Archives 1860-2000
    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 
    ISSN: 1573-2916
    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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    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
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    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 
    ISSN: 1573-2916
    Keywords: Global optimization ; Hölder functions ; Lipschitz optimization
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    Topics: Mathematics
    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 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
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    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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