Optimization: principles and algorithms, by Michel Bierlaire
run1702constrainedNewton.m
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1 %> \file
2 %> Runs example 17.2 of \cite Bier15-book with constrained Newton (Table 17.3)
3 %>
4 %> @note Calls \ref constrainedNewton
5 %>
6 %> @ingroup Running
7 %> @ingroup chap17
8 %> @author Michel Bierlaire
9 %> @date Sun Mar 22 15:10:27 2015
10 
11 A = [-1 1] ;
12 b = -1 ;
13 x0 = [3.5 ; 2.5] ;
14 eps = 1.0e-6 ;
15 [solution, iteres, niter] = constrainedNewton('ex1101',A,b,x0,eps);
16 format short e
17 iteres(1:niter,:)
18 solution
19 format
20 niter
21 
22 
23 
constrainedNewton
function constrainedNewton(in obj, in A, in b, in x0, in eps, in gamma)
Applies the projected gradient method to solve subject to .
Copyright 2015-2018 Michel Bierlaire