Results 251 to 260 of about 694,961 (287)

The method of feasible directions for minimax problems

Optimization, 1992
A general concept of converging algorithms of feasible direction type is introduced using upper approximation functions of the objective. By this means the zigzagging effect can be avoided and convergence to inf-stationary points of the objective is proved.
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An optimization algorithm based on the method of feasible directions

Structural Optimization, 1995
The theory and implementation of an optimization algorithm code based on the method of feasible directions are presented. Although the method of feasible directions was developed during the 1960's, the present implementation of the algorithm includes several modifications to improve its robustness.
A. D. Belegundu, L. Berke, S. N. Patnaik
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On a Method of Feasible Directions for Solving Variational Inequalities

Optimization, 1985
An algorithm of the method of feasible directions is described solving effectively extremal problems which arise by the discretization of variational inequalities. Using the maximum principle the convergence of the algorithm is shown and some numerical examples are given.
H. Kirsten, R. Tichatschke
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Numerical study of some feasible direction methods in mathematical programming

Journal of Optimization Theory and Applications, 1983
Some feasible direction methods for the minimization of a linearly constrained convex function are studied. Special emphasis is placed on the analysis of the procedures which find the search direction, by developing active set methods which use orthogonal or Gauss-Jordan-like transformations.
Arioli M., Laratta A., Menchi O.
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Convergence of methods of feasible directions in extremal problems

USSR Computational Mathematics and Mathematical Physics, 1971
Abstract GENERAL theorems on the convergence conditions of one-step iteration methods for minimization problems with constraints are presented. These theorems are applied for the uniform derivation of both previously known and also new results on the convergence of specific methods. The problem of the minimization of the functional f(x) on the set Q
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Generalized reduced gradient method as an extension of feasible direction methods

Journal of Optimization Theory and Applications, 1977
The paper presents modifications of the generalized reduced gradient method which allows for a convergence proof. For that, a special construction of the basis is introduced, and some tools of the theory of feasible direction are used to modify the common choice of the direction at every step.
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A stochastic approximation counterpart of the feasible direction method

Statistics & Probability Letters, 1987
A stochastic approximation counterpart of the feasible direction method of Topkis and Veinott is considered. No convexity condition on a function to be minimized is imposed and a procedure for one-dimensional minimization along each feasible direction chosen is included.
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Method of feasible directions for solving problems of convex multicriterion optimization

USSR Computational Mathematics and Mathematical Physics, 1987
Summary: A generalization of the method of feasible directions is described for solving problems of convex multicriterion optimization. The method is justified theoretically, and results of numerical experiments are given.
Zhadan, V. G., Kushnirchuk, V. I.
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Optimal elastic design of trusses by feasible direction methods

Journal of Optimization Theory and Applications, 1975
An efficient algorithm, based on Zoutendijk's feasible direction methods, is developed primarily for elastic minimum weight design of trusses subjected to stress, displacement, and cross-sectional constraints. In determining a stepsize in a usable feasible direction, an elaborate procedure is developed to gain as much weight reduction with as little ...
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Methods of centers and methods of feasible directions for the solution of optimal control problems

1971 IEEE Conference on Decision and Control, 1971
This paper shows that extensions of the Frank-Wolfe, Zoutendijk, and Pironneau-Polak algorithms for nonlinear programming problems, can also be used to solve various optimal control problems.
E. Polak, H. Mukai, O. Pironneau
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