Substitute derivatives in unconstrained optimization: A comparison of finite difference and response surface approximations

DOI10.1016/0305-0548(88)90018-4zbMath0643.90078OpenAlexW1980136879MaRDI QIDQ1102201

Jeffrey L. Ringuest

Publication date: 1988

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0305-0548(88)90018-4

zbMATH Keywords

conjugate gradient algorithmsteepest descent algorithmfinite difference approximationexperimental designsquasi-Newton algorithmaccuracy of unconstrained gradient searchfirst- order response surfacessubstitute derivatives

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Numerical methods based on nonlinear programming (49M37)

Related Items (3)

A Modified Quasi‐Newton Method for Optimization in Simulation ⋮ A stochastic quasi-Newton method for simulation response optimization ⋮ Simulation response optimization via direct conjugate direction method

Cites Work

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• Mathematical programming and the optimization of computer simulations
• An Evaluation of Substitute Methods for Derivatives in Unconstrained Optimization
• Design and Testing of a Generalized Reduced Gradient Code for Nonlinear Programming
• A Rapidly Convergent Descent Method for Minimization
• Function minimization by conjugate gradients
• A Family of Variable-Metric Methods Derived by Variational Means
• A new approach to variable metric algorithms
• The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations
• Conditioning of Quasi-Newton Methods for Function Minimization

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