Optimization: principles and algorithms, by Michel Bierlaire
constrainedNewton.m File Reference

Algorithm 17.2: preconditioned projected gradient, or constrained Newton. More...

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## Functions

function constrainedNewton (in obj, in A, in b, in x0, in eps, in gamma)
Applies the projected gradient method to solve subject to . More...

## Detailed Description

Algorithm 17.2: preconditioned projected gradient, or constrained Newton.

Implementation of algorithm 17.2 of [1]

Note
Tested with run1702constrainedNewton.m
Date
Sun Mar 22 16:25:39 2015

Definition in file constrainedNewton.m.

## Function Documentation

 function constrainedNewton ( in obj, in A, in b, in x0, in eps, in gamma )

Applies the projected gradient method to solve subject to .

Parameters
 obj the name of the Octave function defining and . A matrix of the constraint b right-hand side of the constraint x0 starting point eps algorithm stops if . gamma parameter > 0 (default: 1) maxiter maximum number of iterations (default: 100)
Returns
[solution,iteres,niter]
solution: local minimum of the function
iteres: sequence of iterates generated by the algorithm. It contains n+2 columns. Columns 1:n contains the value of the current iterate. Column n+1 contains the value of the objective function. Column n+2 contains the value of the norm of the gradient. It contains maxiter rows, but only the first niter rows are meaningful.
niter: total number of iterations