Optimization: principles and algorithms, by Michel Bierlaire
run1101cauchy.m
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1 %> \file
2 %> Runs example 11.1. It is a specific implementation of the steepest descent algortihm, where tha Cauchy point is calculated at each iteration. It illustrates the high sensitivity of the method to ill-conditioning.(Table 11.1)
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5 %> \note Calls \ref steepestDescentCauchy
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7 %>
8 %> @ingroup Running
9 %> @ingroup chap11
10 %> @author <a href="http://people.epfl.ch/michel.bierlaire">Michel Bierlaire</a>
11 %> @date Fri Mar 20 17:11:29 2015
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13 %>
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15 x0 = [9;1] ;
16 [solution, iteres, niter] = steepestDescentCauchy('ex1101',x0,1.0e-15,1000) ;
17 format short e
18 iteres(1:niter,:)
19 solution
20 format
21 niter
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steepestDescentCauchy
function steepestDescentCauchy(in obj, in x0, in eps, in maxiter)
Applies the steepest descent algorithm to solve where . The Cauchy point is calculated at each it...
Copyright 2015-2018 Michel Bierlaire