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Optimization Transformation from Yalmip to Gurobi

This is a small part of an optimization of Variable P. One of the conditions of this optimization is that two samples within the horizon of 4 samples have to be constant. A solution with Yalmip Code is already available (see below). The goal of the project is to transform this Yalmip Code into fully running Gurobi Model Code (see: [url removed, login to view] and [url removed, login to view]:solving).

The current Matlab Code for Yalmip looks like this:

%Variables

Horizon = 4; %number of total samples

samp_const =2; %number of samples which should be held constant

Pmin = [150,100,100,100]; %Min Constraint

Pmax = [150,200,200,200]; %Max Constraint

P = sdpvar(1,Horizon,'full');

%Constraints

Constraints = []; %Initialize

for k = 1:Horizon

Constraints = [Constraints, Pmin(:,k) <= P(:,k) <= Pmax(:,k)];

end

%Objective Function

Objective = 0; %Initialize

for k = 2:Horizon

range = k:min(Horizon, k+samp_const-1); % indices where no further rate changes is allowed

moves_1 = sum(ceil( abs(P(1,range)-P(1,range-1))./2000 )); % count rate changes in range

Objective = Objective + ...

Constweight*floor(0.81+moves_1/10); % constraint: maximal 1 rate change in 2 (samp_const) samples

end

options = sdpsettings('solver','gurobi');

result = optimize(Constraints,Objective,options);

Gurobi License needed in order to test the code
Matlab License
Experience with Yalmip

Compétences : Algorithme, Matlab and Mathematica

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Concernant l'employeur :
( 0 commentaires ) Austria

N° du projet : #12696207

9 freelance ont fait une offre moyenne de 200 € pour ce travail

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