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 
function constrainedNewton(in obj, in A, in b, in x0, in eps, in gamma)
Applies the projected gradient method to solve subject to .
Copyright 2015-2016 Michel Bierlaire