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mmasub.m
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mmasub.m
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%-------------------------------------------------------------
%
% Copyright (C) 2007, 2008 Krister Svanberg
%
% This file, mmasub.m, is part of GCMMA-MMA-code.
%
% GCMMA-MMA-code is free software; you can redistribute it and/or
% modify it under the terms of the GNU General Public License as
% published by the Free Software Foundation; either version 3 of
% the License, or (at your option) any later version.
%
% This code is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% (file COPYING) along with this file. If not, see
% <http://www.gnu.org/licenses/>.
%
% You should have received a file README along with this file,
% containing contact information. If not, see
% <http://www.smoptit.se/> or e-mail [email protected] or [email protected].
%
% Version September 2007 (and a small change August 2008)
%
%
function [xmma,ymma,zmma,lam,xsi,eta,mu,zet,s,low,upp] = ...
mmasub(m,n,iter,xval,xmin,xmax,xold1,xold2, ...
f0val,df0dx,fval,dfdx,low,upp,a0,a,c,d);
%
% This function mmasub performs one MMA-iteration, aimed at
% solving the nonlinear programming problem:
%
% Minimize f_0(x) + a_0*z + sum( c_i*y_i + 0.5*d_i*(y_i)^2 )
% subject to f_i(x) - a_i*z - y_i <= 0, i = 1,...,m
% xmin_j <= x_j <= xmax_j, j = 1,...,n
% z >= 0, y_i >= 0, i = 1,...,m
%*** INPUT:
%
% m = The number of general constraints.
% n = The number of variables x_j.
% iter = Current iteration number ( =1 the first time mmasub is called).
% xval = Column vector with the current values of the variables x_j.
% xmin = Column vector with the lower bounds for the variables x_j.
% xmax = Column vector with the upper bounds for the variables x_j.
% xold1 = xval, one iteration ago (provided that iter>1).
% xold2 = xval, two iterations ago (provided that iter>2).
% f0val = The value of the objective function f_0 at xval.
% df0dx = Column vector with the derivatives of the objective function
% f_0 with respect to the variables x_j, calculated at xval.
% fval = Column vector with the values of the constraint functions f_i,
% calculated at xval.
% dfdx = (m x n)-matrix with the derivatives of the constraint functions
% f_i with respect to the variables x_j, calculated at xval.
% dfdx(i,j) = the derivative of f_i with respect to x_j.
% low = Column vector with the lower asymptotes from the previous
% iteration (provided that iter>1).
% upp = Column vector with the upper asymptotes from the previous
% iteration (provided that iter>1).
% a0 = The constants a_0 in the term a_0*z.
% a = Column vector with the constants a_i in the terms a_i*z.
% c = Column vector with the constants c_i in the terms c_i*y_i.
% d = Column vector with the constants d_i in the terms 0.5*d_i*(y_i)^2.
%
%*** OUTPUT:
%
% xmma = Column vector with the optimal values of the variables x_j
% in the current MMA subproblem.
% ymma = Column vector with the optimal values of the variables y_i
% in the current MMA subproblem.
% zmma = Scalar with the optimal value of the variable z
% in the current MMA subproblem.
% lam = Lagrange multipliers for the m general MMA constraints.
% xsi = Lagrange multipliers for the n constraints alfa_j - x_j <= 0.
% eta = Lagrange multipliers for the n constraints x_j - beta_j <= 0.
% mu = Lagrange multipliers for the m constraints -y_i <= 0.
% zet = Lagrange multiplier for the single constraint -z <= 0.
% s = Slack variables for the m general MMA constraints.
% low = Column vector with the lower asymptotes, calculated and used
% in the current MMA subproblem.
% upp = Column vector with the upper asymptotes, calculated and used
% in the current MMA subproblem.
%
%epsimin = sqrt(m+n)*10^(-9);
epsimin = 10^(-7);
raa0 = 0.00001;
move = 0.5;
albefa = 0.1;
asyinit = 0.5;
asyincr = 1.2;
asydecr = 0.7;
eeen = ones(n,1);
eeem = ones(m,1);
zeron = zeros(n,1);
% Calculation of the asymptotes low and upp :
if iter < 2.5
low = xval - asyinit*(xmax-xmin);
upp = xval + asyinit*(xmax-xmin);
else
zzz = (xval-xold1).*(xold1-xold2);
factor = eeen;
factor(find(zzz > 0)) = asyincr;
factor(find(zzz < 0)) = asydecr;
low = xval - factor.*(xold1 - low);
upp = xval + factor.*(upp - xold1);
lowmin = xval - 10*(xmax-xmin);
lowmax = xval - 0.01*(xmax-xmin);
uppmin = xval + 0.01*(xmax-xmin);
uppmax = xval + 10*(xmax-xmin);
low = max(low,lowmin);
low = min(low,lowmax);
upp = min(upp,uppmax);
upp = max(upp,uppmin);
end
% Calculation of the bounds alfa and beta :
zzz1 = low + albefa*(xval-low);
zzz2 = xval - move*(xmax-xmin);
zzz = max(zzz1,zzz2);
alfa = max(zzz,xmin);
zzz1 = upp - albefa*(upp-xval);
zzz2 = xval + move*(xmax-xmin);
zzz = min(zzz1,zzz2);
beta = min(zzz,xmax);
% Calculations of p0, q0, P, Q and b.
xmami = xmax-xmin;
xmamieps = 0.00001*eeen;
xmami = max(xmami,xmamieps);
xmamiinv = eeen./xmami;
ux1 = upp-xval;
ux2 = ux1.*ux1;
xl1 = xval-low;
xl2 = xl1.*xl1;
uxinv = eeen./ux1;
xlinv = eeen./xl1;
%
p0 = zeron;
q0 = zeron;
p0 = max(df0dx,0);
q0 = max(-df0dx,0);
pq0 = 0.001*(p0 + q0) + raa0*xmamiinv;
p0 = p0 + pq0;
q0 = q0 + pq0;
p0 = p0.*ux2;
q0 = q0.*xl2;
%
P = sparse(m,n);
Q = sparse(m,n);
P = max(dfdx,0);
Q = max(-dfdx,0);
PQ = 0.001*(P + Q) + raa0*eeem*xmamiinv';
P = P + PQ;
Q = Q + PQ;
P = P * spdiags(ux2,0,n,n);
Q = Q * spdiags(xl2,0,n,n);
b = P*uxinv + Q*xlinv - fval ;
%
%%% Solving the subproblem by a primal-dual Newton method
[xmma,ymma,zmma,lam,xsi,eta,mu,zet,s] = ...
subsolv(m,n,epsimin,low,upp,alfa,beta,p0,q0,P,Q,a0,a,b,c,d);