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Direct Search Optimization Method
Journal of the Structural Division, 1973A nongradient direct search procedure is presented to treat general structural optimization problems, in which the independent design variables are discrete in nature. The procedure combines the basic ideas of the complex method of Box with the pattern search of Hooke and Jeeves.
Ying-San Lai, Jan D. Achenbach
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Direct search methods for nonsmooth optimization
2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601), 2004We present analytical tools that enable development of globally convergent direct search methods for minimization of nonsmooth locally Lipschitz functions. The theory is illustrated with the design of new algorithms.
D. Popovic, A.R. Teel
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A New Direct Search Optimization Method
Journal of Information and Optimization Sciences, 1988Abstract A new direct search nonlinear optimization algorithm is presented. The new algorithm utilizes a specially developed automatic step adjustment algorithm to be used in conjunction with pattern search method developed by Hooke and Jeeves. Two automatic step adjustment schemes arc described. The new algorithm was tested using standard optimization
Samir S. Raouf, Najat M. Rashid
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Bilevel Nash Equilibrium Problems: Numerical Approximation Via Direct-Search Methods
Dynamic Games and Applications, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Francesco Caruso +2 more
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Direct-search penalty/barrier methods
2010In Nonlinear Optimization Penalty and Barrier Methods are normally used to solve Constrained Problems. There are several Penalty/Barrier Methods and they are used in several areas from Engineering to Economy, through Biology, Chemistry, Physics among others.
Correia, Aldina +3 more
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Golden section search over hyper-rectangle: a direct search method
International Journal of Mathematics in Operational Research, 2016This paper generalises the golden section optimal search method to higher dimensional optimisation problem. The method is applicable to a strict quasi-convex function of N-variables over an N-dimensional hyper rectangle. An algorithm is proposed in N-dimension. The algorithm is illustrated graphically in two dimensions and verified through several test
Suvra Kanti Chakraborty +1 more
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1987
In this chapter we shall describe a number of direct search methods for minimization. Such methods do not require the explicit evaluation of any partial derivatives of the function being minimized, but instead rely solely on values of the function found during the iterative process.
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In this chapter we shall describe a number of direct search methods for minimization. Such methods do not require the explicit evaluation of any partial derivatives of the function being minimized, but instead rely solely on values of the function found during the iterative process.
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A Direct Search Method for Nonlinear Programming
ZAMM, 1999Summary: An iterative model algorithm for minimizing a Lipschitz-continuous function subject to continuous constraints is introduced. Each iteration of the method proceeds in two phases. In the first phase, feasibility is improved and, as a result, a more feasible intermediate point is obtained.
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