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  • Articles  (57)
  • numerical methods  (57)
  • Springer  (57)
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  • Frontiers Media
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  • 2020-2022
  • 1980-1984  (12)
  • 1975-1979  (33)
  • 1970-1974  (12)
  • Mathematics  (57)
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  • Articles  (57)
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  • Springer  (57)
  • Cambridge University Press
  • Frontiers Media
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  • 1
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    Journal of nondestructive evaluation 2 (1981), S. 241-247 
    ISSN: 1573-4862
    Keywords: ultrasonic field ; numerical methods ; transducer design ; NDE
    Source: Springer Online Journal Archives 1860-2000
    Topics: Electrical Engineering, Measurement and Control Technology , Mathematics
    Notes: Abstract A simple, flexible, accurate, and comprehensive numerical method is presented for theoretically analyzing the diffraction field of a continuous wave transducer of arbitrary size, shape, and frequency. Using the extensively studied circular transducer for comparison, numerical results are shown for an unfocused transducer with uniform velocity excitation as well as for a focused transducer with Gaussian velocity excitation. Data concerning the execution time, program size, and convergence of the method are also presented for its implementation as a design tool on a minicomputer system.
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  • 2
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    Journal of nondestructive evaluation 4 (1984), S. 39-42 
    ISSN: 1573-4862
    Keywords: Eddy current ; computer analysis ; numerical methods ; NDE
    Source: Springer Online Journal Archives 1860-2000
    Topics: Electrical Engineering, Measurement and Control Technology , Mathematics
    Notes: Abstract Theoretical calculations of eddy-current phenomena often involve the numerical evaluation of various integral expressions. A discussion of some of the possible evaluation methods and of the factors to be considered in choosing a method is presented.
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  • 3
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    Acta applicandae mathematicae 1 (1983), S. 263-279 
    ISSN: 1572-9036
    Keywords: 34C ; 34D ; 65N ; 65P ; 92 ; numerical methods ; bifurcation theory ; enzyme kinetics ; morphogenesis ; Galerkin method
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper deals with the use of the Galerkin approximation for calculating branches of steady-state solutions. It is motivated by the analysis of a reaction-diffusion system modeled by a pair of nonlinear partial differential equations on a two-dimensional domain. The goal is to check the possibility of closed loops emerging from a ‘trivial’ branch. This issue is of importance in recent theories on morphogenesis in embryos (Kauffman et al. [3]). Numerical methods for continuing Galerkin approximations of the steady states give arcs of stable or unstable solutions. The numerical results are in agreement with the predictions of Brezzi et al. [6–8]. In particular, bifurcations from the trivial steady-state or symmetry-breaking bifurcations remain bifurcations for the approximate problem. The whole connected set of solutions thus obtained gives new insight into the behavior of solutions to reaction-diffusion equations and strongly advocates Kauffman's theory.
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  • 4
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    Journal of optimization theory and applications 13 (1974), S. 74-93 
    ISSN: 1573-2878
    Keywords: Stochastic control problems ; numerical methods ; perturbation methods ; suboptimal control ; closed-loop control
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract A numerical technique is described for solving approximately certain small-noise stochastic control problems. The method uses quantities computable from the optimal solution to the corresponding deterministic control problem. Numerical results are given for a two-dimensional linear regular problem with saturation and a time-optimal problem.
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  • 5
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    Journal of optimization theory and applications 13 (1974), S. 179-185 
    ISSN: 1573-2878
    Keywords: Optimal control theory ; functional differential equations ; numerical methods ; hereditary processes ; contraction mapping principle
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This note considers the optimal control of a system represented by nonlinear differential-integral equations with a general cost function. A second-order iterative method of solution based on the fixed-point contraction mapping principle is proposed.
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  • 6
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    Journal of optimization theory and applications 13 (1974), S. 164-178 
    ISSN: 1573-2878
    Keywords: Two-point boundary-value problems ; calculus of variations ; numerical methods ; differential equations ; computing methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract In the parameter variation method, a scalar parameterk, kε[0, 1], is introduced into the differential equations. The parameterk is inserted in such a way that, whenk=0, the solution of the boundary-value problem is known or readily calculated and, whenk=1, the problem is identical with the original problem. Thus, bydeforming the solution step-by-step throughk-space fromk=0 tok=1, the original problem may be solved. These solutions then provide good starting values for any convergent, iterative scheme such as the Newton-Raphson method. The method is applied to the solution of problems with various types of boundary-value specifications and is further extended to take account of situations arising in the solution of problems from variational calculus (e.g., total elapsed time not specified, optimum control not a simple function of the variables).
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  • 7
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    Journal of optimization theory and applications 13 (1974), S. 635-659 
    ISSN: 1573-2878
    Keywords: Nonlinear programming ; numerical methods ; unconstrained minimization ; function minimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper is concerned with the problem of investigating the properties and comparing the methods of nonlinear programming. The steepest-descent method, the method of Davidon, the method of conjugate gradients, and other methods are investigated for the class of essentially nonlinear valley functions.
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  • 8
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    Journal of optimization theory and applications 14 (1974), S. 393-403 
    ISSN: 1573-2878
    Keywords: Augmented penalty function ; penalty function methods ; method of multipliers ; numerical methods ; state variable constraints
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Three augmented penalty function algorithms are tested and compared with an ordinary penalty function algorithm for two demonstration optimal control problems. Although the augmented penalty function is quite helpful in solving control problems with terminal state constraints, the convergence can be improved significantly by providing systematic increases in the penalty constant.
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  • 9
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    Journal of optimization theory and applications 14 (1974), S. 529-556 
    ISSN: 1573-2878
    Keywords: Calculus of variations ; optimal control ; computing methods ; numerical methods ; boundary-value problems ; modified quasilinearization algorithm ; nondifferential constraints
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper considers the numerical solution of optimal control problems involving a functionalI subject to differential constraints, nondifferential constraints, and terminal constraints. The problem is to find the statex(t), the controlu(t), and the parameter π so that the functional is minimized, while the constraints are satisfied to a predetermined accuracy. A modified quasilinearization algorithm is developed. Its main property is the descent property in the performance indexR, the cumulative error in the constraints and the optimality conditions. Modified quasilinearization differs from ordinary quasilinearization because of the inclusion of the scaling factor (or stepsize) α in the system of variations. The stepsize is determined by a one-dimensional search on the performance indexR. Since the first variation δR is negative, the decrease inR is guaranteed if α is sufficiently small. Convergence to the solution is achieved whenR becomes smaller than some preselected value. In order to start the algorithm, some nominal functionsx(t),u(t), π and nominal multipliers λ(t), ρ(t), μ must be chosen. In a real problem, the selection of the nominal functions can be made on the basis of physical considerations. Concerning the nominal multipliers, no useful guidelines have been available thus far. In this paper, an auxiliary minimization algorithm for selecting the multipliers optimally is presented: the performance indexR is minimized with respect to λ(t), ρ(t), μ. Since the functionalR is quadratically dependent on the multipliers, the resulting variational problem is governed by optimality conditions which are linear and, therefore, can be solved without difficulty. To facilitate the numerical solution on digital computers, the actual time θ is replaced by the normalized timet, defined in such a way that the extremal arc has a normalized time length Δt=1. In this way, variable-time terminal conditions are transformed into fixed-time terminal conditions. The actual time τ at which the terminal boundary is reached is regarded to be a component of the parameter π being optimized. The present general formulation differs from that of Ref. 3 because of the inclusion of the nondifferential constraints to be satisfied everywhere over the interval 0⩽t⩽1. Its importance lies in that (i) many optimization problems arise directly in the form considered here, (ii) there are problems involving state equality constraints which can be reduced to the present scheme through suitable transformations, and (iii) there are some problems involving inequality constraints which can be reduced to the present scheme through the introduction of auxiliary variables. Numerical examples are presented for the free-final-time case. These examples demonstrate the feasibility as well as the rapidity of convergence of the technique developed in this paper.
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  • 10
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    Journal of optimization theory and applications 17 (1975), S. 361-430 
    ISSN: 1573-2878
    Keywords: Survey papers ; gradient methods ; numerical methods ; computing methods ; calculus of variations ; optimal control ; gradient-restoration algorithms ; boundary-value problems ; bounded control problems ; bounded state problems ; nondifferential constraints
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper summarizes recent advances in the area of gradient algorithms for optimal control problems, with particular emphasis on the work performed by the staff of the Aero-Astronautics Group of Rice University. The following basic problem is considered: minimize a functionalI which depends on the statex(t), the controlu(t), and the parameter π. Here,I is a scalar,x ann-vector,u anm-vector, and π ap-vector. At the initial point, the state is prescribed. At the final point, the statex and the parameter π are required to satisfyq scalar relations. Along the interval of integration, the state, the control, and the parameter are required to satisfyn scalar differential equations. First, the sequential gradient-restoration algorithm and the combined gradient-restoration algorithm are presented. The descent properties of these algorithms are studied, and schemes to determine the optimum stepsize are discussed. Both of the above algorithms require the solution of a linear, two-point boundary-value problem at each iteration. Hence, a discussion of integration techniques is given. Next, a family of gradient-restoration algorithms is introduced. Not only does this family include the previous two algorithms as particular cases, but it allows one to generate several additional algorithms, namely, those with alternate restoration and optional restoration. Then, two modifications of the sequential gradient-restoration algorithm are presented in an effort to accelerate terminal convergence. In the first modification, the quadratic constraint imposed on the variations of the control is modified by the inclusion of a positive-definite weighting matrix (the matrix of the second derivatives of the Hamiltonian with respect to the control). The second modification is a conjugate-gradient extension of the sequential gradient-restoration algorithm. Next, the addition of a nondifferential constraint, to be satisfied everywhere along the interval of integration, is considered. In theory, this seems to be only a minor modification of the basic problem. In practice, the change is considerable in that it enlarges dramatically the number and variety of problems of optimal control which can be treated by gradient-restoration algorithms. Indeed, by suitable transformations, almost every known problem of optimal control theory can be brought into this scheme. This statement applies, for instance, to the following situations: (i) problems with control equality constraints, (ii) problems with state equality constraints, (iii) problems with equality constraints on the time rate of change of the state, (iv) problems with control inequality constraints, (v) problems with state inequality constraints, and (vi) problems with inequality constraints on the time rate of change of the state. Finally, the simultaneous presence of nondifferential constraints and multiple subarcs is considered. The possibility that the analytical form of the functions under consideration might change from one subarc to another is taken into account. The resulting formulation is particularly relevant to those problems of optimal control involving bounds on the control or the state or the time derivative of the state. For these problems, one might be unwilling to accept the simplistic view of a continuous extremal arc. Indeed, one might want to take the more realistic view of an extremal arc composed of several subarcs, some internal to the boundary being considered and some lying on the boundary. The paper ends with a section dealing with transformation techniques. This section illustrates several analytical devices by means of which a great number of problems of optimal control can be reduced to one of the formulations presented here. In particular, the following topics are treated: (i) time normalization, (ii) free initial state, (iii) bounded control, and (iv) bounded state.
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  • 11
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    Journal of optimization theory and applications 19 (1976), S. 367-379 
    ISSN: 1573-2878
    Keywords: Differential equations ; numerical methods ; quasilinearization methods ; two-point boundary-value problems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper reports on a method of numerical solution of sensitive nonlinear two-point boundary-value problems. The method consists of a modification of the continuation technique in quasilinearization obtained by combination with an orthogonalization procedure for linear boundary-value problems.
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  • 12
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    Journal of optimization theory and applications 20 (1976), S. 1-12 
    ISSN: 1573-2878
    Keywords: Methods of multipliers ; nonlinear programming ; numerical methods ; optimization theorems ; quadratically convergent algorithms
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract In a recent paper (Ref. 1), the author briefly mentioned a variant of Hestenes' method of multipliers which would converge quadratically. This note examines that method in detail and provides some examples. In the quadratic-linear case, this algorithm converges in one iteration.
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  • 13
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    Journal of optimization theory and applications 20 (1976), S. 297-313 
    ISSN: 1573-2878
    Keywords: Mathematical programming ; nonlinear programming ; inequality constraints ; numerical methods ; descent methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper is concerned with first-order methods of feasible directions. Pironneau and Polak have recently proved theorems which show that three of these methods have a linear rate of convergence for certain convex problems in which the objective functions have positive definite Hessians near the solutions. In the present note, it is shown that these theorems on rate of convergence can be extended to larger classes of problems. These larger classes are determined in part by certain second-order sufficiency conditions, and they include many nonconvex problems. The arguments used here are based on the finite-dimensional version of Hestenes' indirect sufficiency method.
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  • 14
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    Journal of optimization theory and applications 21 (1977), S. 213-223 
    ISSN: 1573-2878
    Keywords: Optimal stochastic control ; optimal feedback control ; numerical methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper deals with the computation of optimal feedback control laws for a nonlinear stochastic third-order system in which the nonlinear element is not completely specified. It is shown that, due to the structure of the system, the optimal feedback control law, whenever it exists, is not unique. Also, it is shown that, in order to implement an optimal feedback control law, a nonlinear partial differential equation has to be solved. A finite-difference algorithm for the solution of this equation is suggested, and its efficiency and applicability are demonstrated with examples.
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  • 15
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    Journal of optimization theory and applications 21 (1977), S. 277-297 
    ISSN: 1573-2878
    Keywords: Nonlinear programming algorithms ; numerical methods ; anti-jamming strategies
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract A convergence theory for a class of anti-jamming strategies for nonlinear programming algorithms is presented. This theory generalizes previous results in this area by Zoutendijk, Topkis and Veinott, Mangasarian, and others; it is applicable to algorithms in which the anti-jamming parameter is fixed at some positive value as well as to algorithms in which it tends to zero. In addition, under relatively weak hypotheses, convergence of the entire sequence of iterates is proved.
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  • 16
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    Journal of optimization theory and applications 22 (1977), S. 251-264 
    ISSN: 1573-2878
    Keywords: Team decision theory ; command and control ; organization theory ; integral equations ; numerical methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract The coordination of decisions under uncertainty in a team leads to optimality conditions that are integral equations. A specific example of a two-division firm is developed to illustrate these conditions. Numerical imbedding techniques are used to solve the firm's decision problem. Extensions toward more general techniques and applications are indicated.
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  • 17
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    Journal of optimization theory and applications 22 (1977), S. 239-249 
    ISSN: 1573-2878
    Keywords: Weighted residual methods ; boundary-value problems ; numerical methods ; difference equations ; discrete systems
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    Topics: Mathematics
    Notes: Abstract The foundations, applications, and convergence properties of discrete weighted residual methods (DWRM's) are presented in Refs. 1–3. This paper serves to illustrate DWRM's for solving a sensitive nonlinear discrete boundary-value problem. The results indicate that DWRM's can be applied to provide models of increasing complexity which can then be utilized for the analysis and design of physical systems.
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  • 18
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    Journal of optimization theory and applications 22 (1977), S. 487-508 
    ISSN: 1573-2878
    Keywords: Derivative-free algorithms ; Q-superlinear convergence ; function minimization ; mathematical programming ; numerical methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract A convergence analysis is presented for a general class of derivative-free algorithms for minimizing a functionf(x) for which the analytic form of the gradient and the Hessian is impractical to obtain. The class of algorithms accepts finite-difference approximation to the gradient, with stepsizes chosen in such a way that the length of the stepsize must meet two conditions involving the previous stepsize and the distance from the last estimate of the solution to the current estimate. The algorithms also maintain an approximation to the second-derivative matrix and require that the change inx made at each iteration be subject to a bound that is also revised automatically. The convergence theorems have the features that the starting pointx 1 need not be close to the true solution andf(x) need not be convex. Furthermore, despite the fact that the second-derivative approximation may not converge to the true Hessian at the solution, the rate of convergence is still Q-superlinear. The theorry is also shown to be applicable to a modification of Powell's dog-leg algorithm.
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  • 19
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    Journal of optimization theory and applications 26 (1978), S. 205-242 
    ISSN: 1573-2878
    Keywords: Geometric programming ; algorithms ; comparison of algorithms ; test problems ; numerical methods ; computing methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Numerical results are presented of extensive tests involving five posynomial and twelve signomial programming codes. The set of test problems includes problems with a pure mathematical meaning as well as problems originating from different fields of engineering. The algorithms are compared on the basis of CPU time, number of failures, preparation time, and in-core storage.
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  • 20
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    Journal of optimization theory and applications 26 (1978), S. 395-425 
    ISSN: 1573-2878
    Keywords: Optimal control ; numerical methods ; computing methods ; gradient methods ; gradient-restoration algorithms ; sequential gradient-restoration algorithms ; general boundary conditions ; nondifferential constraints ; bounded control ; bounded state
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper considers the numerical solution of two classes of optimal control problems, called Problem P1 and Problem P2 for easy identification. Problem P1 involves a functionalI subject to differential constraints and general boundary conditions. It consists of finding the statex(t), the controlu(t), and the parameter π so that the functionalI is minimized, while the constraints and the boundary conditions are satisfied to a predetermined accuracy. Problem P2 extends Problem P1 to include nondifferential constraints to be satisfied everywhere along the interval of integration. Algorithms are developed for both Problem P1 and Problem P2. The approach taken is a sequence of two-phase cycles, composed of a gradient phase and a restoration phase. The gradient phase involves one iteration and is designed to decrease the value of the functional, while the constraints are satisfied to first order. The restoration phase involves one or more iterations and is designed to force constraint satisfaction to a predetermined accuracy, while the norm squared of the variations of the control, the parameter, and the missing components of the initial state is minimized. The principal property of both algorithms is that they produce a sequence of feasible suboptimal solutions: the functions obtained at the end of each cycle satisfy the constraints to a predetermined accuracy. Therefore, the values of the functionalI corresponding to any two elements of the sequence are comparable. The stepsize of the gradient phase is determined by a one-dimensional search on the augmented functionalJ, while the stepsize of the restoration phase is obtained by a one-dimensional search on the constraint errorP. The gradient stepsize and the restoration stepsize are chosen so that the restoration phase preserves the descent property of the gradient phase. Therefore, the value of the functionalI at the end of any complete gradient-restoration cycle is smaller than the value of the same functional at the beginning of that cycle. The algorithms presented here differ from those of Refs. 1 and 2, in that it is not required that the state vector be given at the initial point. Instead, the initial conditions can be absolutely general. In analogy with Refs. 1 and 2, the present algorithms are capable of handling general final conditions; therefore, they are suited for the solution of optimal control problems with general boundary conditions. Their importance lies in the fact that many optimal control problems involve initial conditions of the type considered here. Six numerical examples are presented in order to illustrate the performance of the algorithms associated with Problem P1 and Problem P2. The numerical results show the feasibility as well as the convergence characteristics of these algorithms.
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  • 21
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    Journal of optimization theory and applications 28 (1979), S. 1-9 
    ISSN: 1573-2878
    Keywords: Nonlinear optimization ; conjugate-gradient methods ; numerical methods ; computing methods ; mathematical programming ; nonlinear programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Three variants of the classical conjugate-gradient method are presented. Two of these variants are based upon a nonlinear function of a quadratic form. A restarting procedure due to Powell, and based upon some earlier work of Beale, is discussed and incorporated into two of the variants. Results of applying the four algorithms to a set of benchmark problems are included, and some tentative conclusions about the relative merits of the four schemes are presented.
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    Journal of optimization theory and applications 28 (1979), S. 185-212 
    ISSN: 1573-2878
    Keywords: Optimal control ; numerical methods ; computing methods ; transformation techniques ; sequential gradient-restoration algorithm ; nondifferential constraints ; state inequality constraints ; linear state inequality constraints ; partially linear state inequality constraints
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper considers optimal control problems involving the minimization of a functional subject to differential constraints, terminal constraints, and a state inequality constraint. The state inequality constraint is of a special type, namely, it is linear in some or all of the components of the state vector. A transformation technique is introduced, by means of which the inequality-constrained problem is converted into an equality-constrained problem involving differential constraints, terminal constraints, and a control equality constraint. The transformation technique takes advantage of the partial linearity of the state inequality constraint so as to yield a transformed problem characterized by a new state vector of minimal size. This concept is important computationally, in that the computer time per iteration increases with the square of the dimension of the state vector. In order to illustrate the advantages of the new transformation technique, several numerical examples are solved by means of the sequential gradient-restoration algorithm for optimal control problems involving nondifferential constraints. The examples show the substantial savings in computer time for convergence, which are associated with the new transformation technique.
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    Journal of optimization theory and applications 30 (1980), S. 161-179 
    ISSN: 1573-2878
    Keywords: Optimization techniques ; nonlinear programming ; direct methods ; numerical methods ; conjugate directions ; nongradient methods ; ridge-path methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract A modification based on a linearization of a ridge-path optimization method is presented. The linearized ridge-path method is a nongradient, conjugate direction method which converges quadratically in half the number of search directions required for Powell's method of conjugate directions. The ridge-path method and its modification are compared with some basic algorithms, namely, univariate method, steepest descent method, Powell's conjugate direction method, conjugate gradient method, and variable-metric method. The assessment indicates that the ridge-path method, with modifications, could present a promising technique for optimization.
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    Journal of optimization theory and applications 31 (1980), S. 565-581 
    ISSN: 1573-2878
    Keywords: Boundary-value problems ; elliptic control problems ; multigrid methods ; numerical methods ; optimal control
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Elliptic control problems with a quadratic cost functional require the solution of a system of two elliptic boundary-value problems. We propose a fast iterative process for the numerical solution of this problem. The method can be applied to very special problems (for example, Poisson equation for a rectangle) as well as to general equations (arbitrary dimensions, general region). Also, nonlinear problems can be treated. The work required is proportional to the work taken by the numerical solution of a single elliptic equation.
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    Journal of optimization theory and applications 42 (1984), S. 1-17 
    ISSN: 1573-2878
    Keywords: Convergence ; iterative methods ; numerical methods ; characterization of convergence ; linear complementarity problem ; matrix splitting
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Necessary and sufficient conditions are established for the convergence of various iterative methods for solving the linear complementarity problem. The fundamental tool used is the classical notion of matrix splitting in numerical analysis. The results derived are similar to some well-known theorems on the convergence of iterative methods for square systems of linear equations. An application of the results to a strictly convex quadratic program is also given.
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  • 26
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    Journal of optimization theory and applications 44 (1984), S. 269-302 
    ISSN: 1573-2878
    Keywords: Ocean test structures ; offshore structures ; wave kinematics ; identification problems ; parameter identification problems ; wave parameter identification problems ; numerical methods ; computing methods ; mathematical programming ; minimization of functions ; quadratic functions ; linear equations ; least-square problems ; global or strong accuracy ; local or weak accuracy ; integral accuracy ; condition number
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper deals with the solution of the wave parameter identification problem for ocean test structure data. A continuous formulation is assumed. An ocean test structure is considered, and wave elevation and velocities are assumed to be measured with a number of sensors. Within the frame of linear wave theory, a Fourier series model is chosen for the wave elevation and velocities. Then, the following problem is posed: Find the amplitudes of the various wave components of specified frequency and direction, so that the assumed model of wave elevation and velocities provides the best fit to the measured data. Here, the term best fit is employed in the least-square sense over a given time interval. At each time instant, the wave representation involves three indexes (frequency, direction, instrument); hence, three-dimensional arrays are required. This formal difficulty can be avoided by switching to an alternative representation involving only two indexes (frequency-direction, instrument); hence, standard vector-matrix notation can be used. Within this frame, optimality conditions are derived for the amplitudes of the assumed wave model. Numerical results are presented. The effect of various system parameters (number of frequencies, number of directions, sampling time, number of sensors, and location of sensors) is investigated in connection with global or strong accuracy, local or weak accuracy, integral accuracy, and condition number of the system matrix. From the numerical experiments, it appears that the identification problem has a unique solution if the number of directions is smaller than or equal to the number of sensors; it has an infinite number of solutions otherwise. In the case where a unique solution exists, the condition number of the system matrix increases as the size of the system increases, and this has a detrimental effect on the accuracy. However, the accuracy can be improved by proper selection of the sampling time and by proper choice of the number and location of the sensors.
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  • 27
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    Journal of optimization theory and applications 44 (1984), S. 453-484 
    ISSN: 1573-2878
    Keywords: Ocean test structures ; offshore structures ; wave kinematics ; identification problems ; parameter identification problems ; wave parameter identification problems ; numerical methods ; computing methods ; mathematical programming ; minimization of functions ; quadratic functions ; linear equations ; least-square problems ; Householder transformation ; global or strong accuracy ; local or weak accuracy ; integral accuracy ; condition number
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    Notes: Abstract This paper deals with the solution of the wave parameter identification problem for ocean test structure data. A discrete formulation is assumed. An ocean test structure is considered, and wave elevation and velocities are assumed to be measured with a number of sensors. Within the frame of linear wave theory, a Fourier series model is chosen for the wave elevation and velocities. Then, the following problem is posed: Find the amplitudes of the various wave components of specified frequency and direction, so that the assumed model of wave elevation and velocities provides the best fit to the measured data. Here, the term best fit is employed in the least-square sense over a given time interval. At each time instant, the wave representation involves four indexes (frequency, direction, instrument, time); hence, four-dimensional arrays are required. This formal difficulty can be avoided by switching to an alternative representation involving only two indexes (frequency-direction, instrument-time); hence, standard vector-matrix notation can be used. Within this frame, optimality conditions are derived for the amplitudes of the assumed wave model. A characteristic of the wave parameter identification problem is that the condition number of the system matrix can be large. Therefore, the numerical solution is not an easy task and special procedures must be employed. Specifically, Gaussian elimination is avoided and advantageous use is made of the Householder transformation, in the light of the least-square nature of the problem and the discretized approach to the problem. Numerical results are presented. The effect of various system parameters (number of frequencies, number of directions, sampling time, number of sensors, and location of sensors) is investigated in connection with global or strong accuracy, local or weak accuracy, integral accuracy, and condition number of the system matrix. From the numerical experiments, it appears that the wave parameter identification problem has a unique solution if the number of directions is smaller than or equal to the number of sensors; it has an infinite number of solutions otherwise. In the case where a unique solution exists, the condition number of the system matrix increases as the size of the system increases, and this has a detrimental effect on the accuracy. However, the accuracy can be improved by proper selection of the sampling time and by proper choice of the number and location of the sensors. Generally speaking, the computations done for the discrete case exhibit better accuracy than the computations done for the continuous case (Ref. 5). This improved accuracy is a direct consequence of having used advantageously the Householder transformation and is obtained at the expense of increased memory requirements and increased CPU time.
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    Periodica mathematica Hungarica 10 (1979), S. 207-215 
    ISSN: 1588-2829
    Keywords: Primary 65N30 ; Partial differential equations ; boundary value problems ; numerical methods
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    Topics: Mathematics
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    Journal of optimization theory and applications 13 (1974), S. 218-255 
    ISSN: 1573-2878
    Keywords: Calculus of variations ; optimal control ; computing methods ; numerical methods ; gradient methods ; seqential gradient-restoration algorithm ; restoration algorithm ; boundary-value problems ; bounded control problems ; bounded state problems ; nondifferential constraints
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    Notes: Abstract This paper considers the numerical solution of optimal control problems involving a functionalI subject to differential constraints, nondifferential constraints, and terminal constraints. The problem is to find the statex(t), the controlu(t), and the parameter π so that the functional is minimized, while the constraints are satisfied to a predetermined accuracy. The approach taken is a sequence of two-phase processes or cycles, composed of a gradient phase and a restoration phase. The gradient phase involves a single iteration and is designed to decrease the functional, while the constraints are satisfied to first order. The restoration phase involves one or several iterations and is designed to restore the constraints to a predetermined accuracy, while the norm of the variations of the control and the parameter is minimized. The principal property of the algorithm is that it produces a sequence of feasible suboptimal solutions: the functionsx(t),u(t), π obtained at the end of each cycle satisfy the constraints to a predetermined accuracy. Therefore, the functionals of any two elements of the sequence are comparable. The stepsize of the gradient phase is determined by a one-dimensional search on the augmented functionalJ, and the stepsize of the restoration phase by a one-dimensional search on the constraint errorP. If α g is the gradient stepsize and α r is the restoration stepsize, the gradient corrections are ofO(α g ) and the restoration corrections are ofO(α r α g 2). Therefore, for α g sufficiently small, the restoration phase preserves the descent property of the gradient phase: the functionalÎ at the end of any complete gradient-restoration cycle is smaller than the functionalI at the beginning of the cycle. To facilitate the numerical solution on digital computers, the actual time ϑ is replaced by the normalized timet, defined in such a way that the extremal arc has a normalized time length Δt=1. In this way, variable-time terminal conditions are transformed into fixed-time terminal conditions. The actual time τ at which the terminal boundary is reached is regarded to be a component of the parameter π being optimized. The present general formulation differs from that of Ref. 4 because of the inclusion of the nondifferential constraints to be satisfied everywhere over the interval 0 ≤t ≤ 1. Its importance lies in that (i) many optimization problems arise directly in the form considered here, (ii) problems involving state equality constraints can be reduced to the present scheme through suitable transformations, and (iii) problems involving inequality constraints can be reduced to the present scheme through suitable transformations. The latter statement applies, for instance, to the following situations: (a) problems with bounded control, (b) problems with bounded state, (c) problems with bounded time rate of change of the state, and (d) problems where some bound is imposed on an arbitrarily prescribed function of the parameter, the control, the state, and the time rate of change of the state. Numerical examples are presented for both the fixed-final-time case and the free-final-time case. These examples demonstrate the feasibility as well as the rapidity of convergence of the technique developed in this paper.
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    Journal of optimization theory and applications 14 (1974), S. 263-270 
    ISSN: 1573-2878
    Keywords: Two-point boundary-value problems ; differential equations ; Newton-Raphson methods ; computing methods ; numerical methods
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    Notes: Abstract A method based on matching a zero of the right-hand side of the differential equations, in a two-point boundary-value problem, to the boundary conditions is suggested. Effectiveness of the procedure is tested on three nonlinear, two-point boundary-value problems.
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    Journal of optimization theory and applications 18 (1976), S. 425-428 
    ISSN: 1573-2878
    Keywords: Nonlinear programming ; unidimensional search ; numerical methods
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    Notes: Abstract If the functionf to be minimized is not unimodal, the Fibonacci search and the golden section search can determine final search intervals where the functionf takes greater values than at the borders of the first search interval. This can be avoided by small modifications for sequential search procedures where, in each iteration step,f is evaluated at two interior points of the present search interval. The properties of the point of concentration of the search intervals are given.
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    Journal of optimization theory and applications 19 (1976), S. 487-497 
    ISSN: 1573-2878
    Keywords: Laplace transform ; Laplace transform inversion ; numerical methods ; approximation theory
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    Notes: Abstract A function $$\bar f$$ (p) of the Laplace transform operatorp is approximated by a finite linear combination of functions $$\bar \phi $$ (p+α r ), where $$\bar \phi $$ (p) is a specific function ofp having a known analytic inverse φ(t), and is chosen in accordance with various considerations. Then parameters α r ,r=1, 2,...,n, and then corresponding coefficientsA r of the $$\bar \phi $$ (p + α r ) are determined by a least-square procedure. Then, the corresponding approximation to the inversef(t) of $$\bar f$$ (p) is given by analytic inversion of Σ r=1 n A r $$\bar \phi $$ (p+α r ). The method represents a generalization of a method of best rational function approximation due to the author [which corresponds to the particular choice φ(t)≡1], but is capable of yielding considerably greater accuracy for givenn.
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    Journal of optimization theory and applications 20 (1976), S. 37-46 
    ISSN: 1573-2878
    Keywords: Boundary-value problems ; initial-value methods ; differential equations ; numerical methods ; computing methods
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    Notes: Abstract The paper discusses the solution of boundary-value problems for ordinary differential equations by Warner's algorithm. This shooting algorithm requires that only the original system of differential equations is solved once in each iteration, while the initial conditions for a new iteration are evaluated from a matrix equation. Numerical analysis performed shows that the algorithm converges even for very bad starting values of the unknown initial conditions and that the number of iterations is small and weakly dependent on the starting point. Based on this algorithm, a general subroutine can be realized for the solution of a large class of boundary-value problems.
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  • 34
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    Journal of optimization theory and applications 20 (1976), S. 315-329 
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    Keywords: Unconstrained minimization ; variable-metric methods ; numerical methods ; optimization theorems ; function minimization
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    Notes: Abstract The effect of nonlinearly scaling the objective function on the variable-metric method is investigated, and Broyden's update is modified so that a property of invariancy to the scaling is satisfied. A new three-parameter class of updates is generated, and criteria for an optimal choice of the parameters are given. Numerical experiments compare the performance of a number of algorithms of the resulting class.
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    Journal of optimization theory and applications 20 (1976), S. 269-295 
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    Keywords: Mathematical programming ; nonlinear programming ; inequality constraints ; numerical methods ; descent methods
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    Notes: Abstract The method of centers is a well-known method for solving nonlinear programming problems having inequality constraints. Pironneau and Polak have recently presented a new version of this method. In the new method, the direction of search is obtained, at each iteration, by solving a convex quadratic programming problem. This direction finding subprocedure is essentially insensitive to the dimension of the space on which the problem is defined. Moreover, the method of Pironneau and Polak is known to converge linearly for finite-dimensional convex programs for which the objective function has a positive-definite Hessian near the solution (and for which the functions involved are twice continuously differentiable). In the present paper, the method and a completely implementable version of it are shown to converge linearly for a very general class of finite-dimensional problems; the class is determined by a second-order sufficiency condition and includes both convex and nonconvex problems. The arguments employed here are based on the indirect sufficiency method of Hestenes. Furthermore, the arguments can be modified to prove linear convergence for a certain class of infinite-dimensional convex problems, thus providing an answer to a conjecture made by Pironneau and Polak.
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    Journal of optimization theory and applications 20 (1976), S. 455-479 
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    Keywords: Optimal control ; numerical methods ; computing methods ; gradient methods ; quasi-Newton algorithms ; bounded control problems ; singular arcs
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    Notes: Abstract Two existing function-space quasi-Newton algorithms, the Davidon algorithm and the projected gradient algorithm, are modified so that they may handle directly control-variable inequality constraints. A third quasi-Newton-type algorithm, developed by Broyden, is extended to optimal control problems. The Broyden algorithm is further modified so that it may handle directly control-variable inequality constraints. From a computational viewpoint, dyadic operator implementation of quasi-Newton methods is shown to be superior to the integral kernel representation. The quasi-Newton methods, along with the steepest descent method and two conjugate gradient algorithms, are simulated on three relatively simple (yet representative) bounded control problems, two of which possess singular subarcs. Overall, the Broyden algorithm was found to be superior. The most notable result of the simulations was the clear superiority of the Broyden and Davidon algorithms in producing a sharp singular control subarc.
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    Journal of optimization theory and applications 22 (1977), S. 51-61 
    ISSN: 1573-2878
    Keywords: Differential games ; dynamic programming ; Riccati equation ; numerical methods
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    Notes: Abstract We present a monotone iterative technique for the computation of a solution of a Riccati-type equation relevant to the theory of differential games. For this purpose, we show that the Kleinman algorithm for Riccati equation computations converges under extremely general conditions.
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    Journal of optimization theory and applications 23 (1977), S. 229-243 
    ISSN: 1573-2878
    Keywords: Gradient methods ; ill-posed problems ; computing methods ; numerical methods ; failure analysis
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    Notes: Abstract An extensive failure analysis of the steepest-descent optimization algorithm has been made. Each of the ways in which the algorithm can fail is discussed in terms of both the mathematical and numerical manifestations of a failure and the information which each type of failure provides about the formulation of the physical problem. Numerical tests for each of the various types of failure are described; several faulty problem formulations are presented, each of which illustrates a particular type of failure. A table is presented in which all failure modes are summarized and the corresponding numerical tests are exhibited.
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    Journal of optimization theory and applications 23 (1977), S. 471-471 
    ISSN: 1573-2878
    Keywords: Nonlinear programming ; unidemensional search ; numerical methods
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    Notes: Abstract A correction of the procedure of Ref. 1 is given.
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    Journal of optimization theory and applications 24 (1978), S. 5-27 
    ISSN: 1573-2878
    Keywords: Fredholm integral equations ; iterative methods ; Neumann series ; numerical methods ; transport theory
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    Notes: Abstract The present paper extends the synthetic method of transport theory to a large class of integral equations. Convergence and divergence properties of the algorithm are studied analytically, and numerical examples are presented which demonstrate the expected theoretical behavior. It is shown that, in some instances, the computational advantage over the familiar Neumann approach is substantial.
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  • 41
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    Journal of optimization theory and applications 24 (1978), S. 29-57 
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    Keywords: Integral equations ; numerical methods ; imbedding methods
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    Notes: Abstract During the last decade or two, significant progress has been made in the development of imbedding methods for the analytical and computational treatment of integral equations. These methods are now well known in radiative transfer, neutron transport, optimal filtering, and other fields. In this review paper, we describe the current status of imbedding methods for integral equations. The paper emphasizes new analytical and computational developments in control and filtering, multiple scattering, inverse problems of wave propagation, and solid and fluid mechanics. Efficient computer programs for the determination of complex eigenvalues of integral operators, analytical investigations of stability for significant underlying Riccati integrodifferential equations, and comparisons against other methods are described.
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    Journal of optimization theory and applications 24 (1978), S. 153-167 
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    Keywords: Fredholm integral operators ; algebraic invariants ; control theory ; Riccati equation ; numerical methods
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    Notes: Abstract Using the well-known and specific connections between Fredholm integral equations, two-point boundary-value problems, and linear dynamics-quadratic cost control processes, we present a complete, independent set of algebraic invariants suitable for classifying a wide range of Fredholm integral operators with respect to a certain group of transformations. The group, termed theRiccati group, is naturally suggested by the control theoretic setting, but seems nonintuitive from a purely integral-equations point of view. Computational considerations resulting from this classification are discussed.
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    Journal of optimization theory and applications 24 (1978), S. 133-151 
    ISSN: 1573-2878
    Keywords: Integral equations ; numerical methods ; equivalent differential systems ; Volterra integral equations ; initial-value methods
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    Notes: Abstract A method of converting nonlinear Volterra equations to systems of ordinary differential equations is compared with a standard technique, themethod of moments, for linear Fredholm equations. The method amounts to constructing a Galerkin approximation when the kernel is either finitely decomposable or approximated by a certain Fourier sum. Numerical experiments from recent work by Bownds and Wood serve to compare several standard approximation methods as they apply to smooth kernels. It is shown that, if the original kernel decomposes exactly, then the method produces a numerical solution which is as accurate as the method used to solve the corresponding differential system. If the kernel requires an approximation, the error is greater, but in examples seems to be around 0.5% for a reasonably small number of approximating terms. In any case, the problem of excessive kernel evaluations is circumvented by the conversion to the system of ordinary differential equations.
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    Journal of optimization theory and applications 24 (1978), S. 207-220 
    ISSN: 1573-2878
    Keywords: Stereology ; statistics ; packing problem ; Abel integral equations ; Volterra integral equations ; numerical methods
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    Notes: Abstract We discuss the application of integral equations techniques to two broad areas of particle statistics, namely, stereology and packing. Problems in stereology lead to the inversion of Abel-type integral equations; and we present a brief survey of existing methods, analytical and numerical, for doing this. Packing problems lead to Volterra equations which, in simple cases, can be solved exactly and, in other cases, need to be solved numerically. Methods for doing this are presented along with some numerical results.
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    Journal of optimization theory and applications 24 (1978), S. 221-232 
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    Keywords: Integral equations ; numerical methods ; differential equations ; numerical integration
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    Notes: Abstract An initial-value method of Bownds for solving Volterra integral equations is reexamined using a variable-step integrator to solve the differential equations. It is shown that such equations may be easily solved to an accuracy ofO(10−8), the error depending essentially on that incurred in truncating expansions of the kernel to a degenerate one.
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    Journal of optimization theory and applications 25 (1978), S. 335-347 
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    Keywords: Function minimization ; nonlinear constraints ; nonlinear approximation ; numerical methods
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    Notes: Abstract An iterative technique is developed to solve the problem of minimizing a functionf(y) subject to certain nonlinear constraintsg(y)=0. The variables are separated into the basic variablesx and the independent variablesu. Each iteration consists of a gradient phase and a restoration phase. The gradient phase involves a movement (on a surface that is linear in the basic variables and nonlinear in the independent variables) from a feasible point to a varied point in a direction based on the reduced gradient. The restoration phase involves a movement (in a hyperplane parallel tox-space) from the nonfeasible varied point to a new feasible point. The basic scheme is further modified to implement the method of conjugate gradients. The work required in the restoration phase is considerably reduced when compared with the existing methods.
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    Journal of optimization theory and applications 13 (1974), S. 303-318 
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    Keywords: Differential games ; closed-loop controls ; numerical methods ; optimal strategies ; zero-sum games
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    Notes: Abstract This paper presents a method for generating nearoptimal closed-loop solutions to zero-sum perfect information differential games with and without the final time explicitly specified, and with and without control constraints. This near-optimal closed-loop solution is generated by periodically updating the solution to the two-point boundary-value problem obtained by the application of the necessary conditions for a saddle-point solution. The resulting updated open-loop control is then used between updating intervals. Three examples are presented to illustrate the application of this method.
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    Journal of optimization theory and applications 13 (1974), S. 620-634 
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    Keywords: Mathematical programming ; function minimization ; method of dual matrices ; computing methods ; numerical methods
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    Notes: Abstract In Ref. 2, four algorithms of dual matrices for function minimization were introduced. These algorithms are characterized by the simultaneous use of two matrices and by the property that the one-dimensional search for the optimal stepsize is not needed for convergence. For a quadratic function, these algorithms lead to the solution in at mostn+1 iterations, wheren is the number of variables in the function. Since the one-dimensional search is not needed, the total number of gradient evaluations for convergence is at mostn+2. In this paper, the above-mentioned algorithms are tested numerically by using five nonquadratic functions. In order to investigate the effects of the stepsize on the performances of these algorithms, four schemes for the stepsize factor are employed, two corresponding to small-step processes and two corresponding to large-step processes. The numerical results show that, in spite of the wide range employed in the choice of the stepsize factor, all algorithms exhibit satisfactory convergence properties and compare favorably with the corresponding quadratically convergent algorithms using one-dimensional searches for optimal stepsizes.
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    Journal of optimization theory and applications 37 (1982), S. 315-341 
    ISSN: 1573-2878
    Keywords: Optimization ; unconstrained minimization ; updates ; line searches ; convergence ; numerical methods
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    Notes: Abstract We consider a certain generalization of the Huang family of updates and discuss, firstly, convergence, dependence on parameters, and descent property; secondly, invariance under nonlinear scaling, conjugacy of search directions, and possibility of achieving a better approximation of the inverse of the Hessian. The last three aspects are shown to be dependent on particular choices of parameters. A numerical experiment is presented comparing the performances of the usual and modified BFGS algorithms.
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    Journal of optimization theory and applications 38 (1982), S. 111-135 
    ISSN: 1573-2878
    Keywords: Minimax problems ; minimax function ; minimax function depending on the state ; minimax function depending on the control ; optimal control ; minimax optimal control ; numerical methods ; computing methods ; transformation techniques ; gradient-restoration algorithms ; sequential gradient-restoration algorithms
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    Notes: Abstract In a previous paper (Part 1), we presented general transformation techniques useful to convert minimax problems of optimal control into the Mayer-Bolza problem of the calculus of variations [Problem (P)]. We considered two types of minimax problems: minimax problems of Type (Q), in which the minimax function depends on the state and does not depend on the control; and minimax problems of Type (R), in which the minimax function depends on both the state and the control. Both Problem (Q) and Problem (R) can be reduced to Problem (P). In this paper, the transformation techniques presented in Part 1 are employed in conjunction with the sequential gradient-restoration algorithm for solving optimal control problems on a digital computer. Both the single-subarc approach and the multiple-subarc approach are employed. Three test problems characterized by known analytical solutions are solved numerically. It is found that the combination of transformation techniques and sequential gradient-restoration algorithm yields numerical solutions which are quite close to the analytical solutions from the point of view of the minimax performance index. The relative differences between the numerical values and the analytical values of the minimax performance index are of order 10−3 if the single-subarc approach is employed. These relative differences are of order 10−4 or better if the multiple-subarc approach is employed.
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    Journal of optimization theory and applications 38 (1982), S. 97-109 
    ISSN: 1573-2878
    Keywords: Minimax problems ; minimax function ; minimax function depending on the state ; minimax function depending on the control ; optimal control ; minimax optimal control ; numerical methods ; computing methods ; transformation techniques ; gradient-restoration algorithms ; sequential gradient-restoration algorithms
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    Notes: Abstract This paper contains general transformation techniques useful to convert minimax problems of optimal control into the Mayer-Bolza problem of the calculus of variations [Problem (P)]. We consider two types of minimax problems: minimax problems of Type (Q), in which the minimax function depends on the state and does not depend on the control; and minimax problems of Type (R), in which the minimax function depends on both the state and the control. Both Problem (Q) and Problem (R) can be reduced to Problem (P). For Problem (Q), we exploit the analogy with a bounded-state problem in combination with a transformation of the Jacobson type. This requires the proper augmentation of the state vectorx(t), the control vectoru(t), and the parameter vector π, as well as the proper augmentation of the constraining relations. As a result of the transformation, the unknown minimax value of the performance index becomes a component of the parameter vector being optimized. For Problem (R), we exploit the analogy with a bounded-control problem in combination with a transformation of the Valentine type. This requires the proper augmentation of the control vectoru(t) and the parameter vector π, as well as the proper augmentation of the constraining relations. As a result of the transformation, the unknown minimax value of the performance index becomes a component of the parameter vector being optimized. In a subsequent paper (Part 2), the transformation techniques presented here are employed in conjunction with the sequential gradient-restoration algorithm for solving optimal control problems on a digital computer; both the single-subarc approach and the multiple-subarc approach are discussed.
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    Journal of optimization theory and applications 16 (1975), S. 429-445 
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    Keywords: Mathematical programming ; conjugate-gradient methods ; variable-metric methods ; linear equations ; numerical methods ; computing methods
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    Notes: Abstract A computationally stable method for the general solution of a system of linear equations is given. The system isA Tx−B=0, where then-vectorx is unknown and then×q matrixA and theq-vectorB are known. It is assumed that the matrixA T and the augmented matrix [A T,B] are of the same rankm, wherem≤n, so that the system is consistent and solvable. Whenm〈n, the method yields the minimum modulus solutionx m and a symmetricn ×n matrixH m of rankn−m, so thatx=x m+H my satisfies the system for ally, ann-vector. Whenm=n, the matrixH m reduces to zero andx m becomes the unique solution of the system. The method is also suitable for the solution of a determined system ofn linear equations. When then×n coefficient matrix is ill-conditioned, the method can produce a good solution, while the commonly used elimination method fails.
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    Journal of optimization theory and applications 35 (1981), S. 1-7 
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    Keywords: Mathematical programming ; control theory ; numerical methods ; convergence theorems ; stability theorems ; discrete-time dynamical systems
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    Notes: Abstract In this paper, stability properties of 2×2 hypermatrices of the form $$\left[ {\begin{array}{*{20}c} { \in {\rm A} \in {\rm B}} \\ {CD} \\ \end{array} } \right]$$ are investigated, where ε is a small parameter. Stability theorems on these and similar matrices play an important role in the convergence analysis of certain numerical methods of mathematical programming and control theory, as pointed out in Refs. 1–3.
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    Journal of optimization theory and applications 13 (1974), S. 519-537 
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    Keywords: Mathematical programming ; function minimization ; method of dual matrices ; computing methods ; numerical methods
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    Notes: Abstract In this paper, the method of dual matrices for the minimization of functions is introduced. The method, which is developed on the model of a quadratic function, is characterized by two matrices at each iteration. One matrix is such that a linearly independent set of directions can be generated, regardless of the stepsize employed. The other matrix is such that, at the point where the first matrix fails to yield a gradient linearly independent of all the previous gradients, it generates a displacement leading to the minimal point. Thus, the one-dimensional search is bypassed. For a quadratic function, it is proved that the minimal point is obtained in at mostn + 1 iterations, wheren is the number of variables in the function. Since the one-dimensional search is not needed, the total number of gradient evaluations for convergence is at mostn + 2. Three algorithms of the method are presented. A reverse algorithm, which permits the use of only one matrix, is also given. Considerations pertaining to the applications of this method to the minimization of a quadratic function and a nonquadratic function are given. It is believed that, since the one-dimensional search can be bypassed, a considerable amount of computational saving can be achieved.
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  • 55
    Electronic Resource
    Electronic Resource
    Springer
    Journal of optimization theory and applications 14 (1974), S. 233-250 
    ISSN: 1573-2878
    Keywords: Time-optimal control ; decomposition methods ; two-point boundary-value problems ; trajectories ; numerical methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract A decomposition technique is presented for minimum-time trajectories which are characterized by intermediate constraints and discontinuities. The optimization of such multiple are trajectories is usually a formidable task. One optimization method, trajectory decomposition, breaks the original trajectory at points of discontinuity into separate arcs and then optimizes each are subject to prescribed boundary conditions. This constitutes a first level of control. Each first-level solution is evaluated by a second-level controller, which iteratively specifies new are boundary conditions in order to achieve an optimum solution. Unfortunately, this two-level method cannot be applied directly to minimum-time trajectories. The two-level trajectory decomposition method is extended here to a three-level technique for treating the minimum-time trajectory. The first level again optimizes each are for specified intervention parameters. The new second level, the time interface controller, exploits certain homogeneity properties to satisfy time transversality conditions at all boundaries and to couple the first-level solution arcs in time. The third level, the state interface controller, satisfies state transversality conditions at the arc junctions iteratively while driving the trajectory to its optimum. The new three-level procedure represents a feasible decomposition because each solution trajectory in the iterative sequence is physically realizable. The technique also offers a decentralization of control effort and reduction of initial-value sensitivities. An example problem is formulated.
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  • 56
    Electronic Resource
    Electronic Resource
    Springer
    Journal of optimization theory and applications 18 (1976), S. 235-257 
    ISSN: 1573-2878
    Keywords: Control theory ; singular problems ; numerical methods ; aerospace engineering ; economic planning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Algorithms for calculating the junction points between optimal nonsingular and singular subarcs of singular control problems are developed. The algorithms consist in formulating appropriate initialvalue and boundary-value problems; the boundary-value problems are solved with the method of multiple shooting. Two examples are detailed to illustrate the proposed numerical methods.
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  • 57
    Electronic Resource
    Electronic Resource
    Springer
    Journal of optimization theory and applications 16 (1975), S. 447-485 
    ISSN: 1573-2878
    Keywords: Nonlinear programming ; mathematical programming ; quadratically convergent algorithms ; conjugate-gradient methods ; variable-metric methods ; computing methods ; numerical methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract The problem of minimizing a functionf(x) subject to the constraint ϕ(x)=0 is considered. Here,f is a scalar,x is ann-vector, and ϕ is anm-vector, wherem 〈n. A general quadratically convergent algorithm is presented. The conjugate-gradient algorithm and the variable-metric algorithms for constrained function minimization can be obtained as particular cases of the general algorithm. It is shown that, for a quadratic function subject to a linear constraint, all the particular algorithms behave identically if the one-dimensional search for the stepsize is exact. Specifically, they all produce the same sequence of points and lead to the constrained minimal point in no more thann −r descent steps, wherer is the number of linearly independent constraints. The algorithms are then modified so that they can also be employed for a nonquadratic function subject to a nonlinear constraint. Some particular algorithms are tested through several numerical examples.
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