Optimization

It introduces several methods to minimize an objective function subject to no constraint or some constraints and applies them to solve an optimization problem for practice and crosscheck. It covers several unconstrained optimization techniques such as the golden search method, the quadratic approxim...

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Bibliographic Details
Published inApplied Numerical Methods Using MATLAB pp. 321 - 370
Format Book Chapter
LanguageEnglish
Published Hoboken, NJ, USA John Wiley & Sons, Inc 14.01.2005
Subjects
conjugate gradient method
constrained optimization
genetic algorithm
golden search method
Lagrange multiplier method
linear programming (LP)
Nelder‐Mead method
Newton method
optimization
penalty function method
quadratic approximation method
simulated annealing
steepest‐descent method
unconstrained optimization
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Summary:It introduces several methods to minimize an objective function subject to no constraint or some constraints and applies them to solve an optimization problem for practice and crosscheck. It covers several unconstrained optimization techniques such as the golden search method, the quadratic approximation method, Nelder‐Mead method, the steepest descent method, Newton method, simulated‐annealing (SA) method, and genetic algorithm (GA). As for constrained optimization, it only introduces MATLAB built‐in routines together with routines for unconstrained optimization.
ISBN:9780471698333
0471698334
DOI:10.1002/0471705195.ch7