Undefined function or variable 'optimproblem'

Hi; please I try to run the program of " Cutting Stock Problem: Problem-Based " to understand column generation algorithm , but I had this error "Undefined function or variable 'optimproblem'", you find below the program:

logLength = 40;
lengthlist = [8; 12; 16; 20];
quantity = [90; 111; 55; 30];
nLengths = length(lengthlist);
patterns = diag(floor(logLength./lengthlist));
nPatterns = size(patterns,2);
subproblem = optimproblem();
cuts = optimvar('cuts', nLengths, 1, 'Type','integer','LowerBound',zeros(nLengths,1));
subproblem.Constraints = dot(lengthlist,cuts) <= logLength;
lpopts = optimoptions('linprog','Display','off');
ipopts = optimoptions('intlinprog',lpopts);
reducedCost = -inf;
reducedCostTolerance = -0.0001;
exitflag = 1;
while reducedCost < reducedCostTolerance && exitflag > 0
    logprob = optimproblem('Description','Cut Logs');     
    % Create variables representing the number of each pattern used
    x = optimvar('x', nPatterns, 1, 'LowerBound', 0);
    % The objective is the number of logs used
    logprob.Objective.logsUsed = sum(x);
    % The constraint is that the cuts satisfy the demand
    logprob.Constraints.Demand = patterns*x >= quantity;

      [values,nLogs,exitflag,~,lambda] = solve(logprob,'options',lpopts);

      if exitflag > 0
          fprintf('Using %g logs\n',nLogs);
          % Now generate a new pattern, if possible
          subproblem.Objective = 1.0 - dot(lambda.Constraints.Demand,cuts);
          [values,reducedCost,pexitflag] = solve(subproblem,'options',ipopts);
          newpattern = round(values.cuts);
          if double(pexitflag) > 0 && reducedCost < reducedCostTolerance
              patterns = [patterns newpattern];
              nPatterns = nPatterns + 1;
          end
      end
  end
  if exitflag <= 0 
      disp('Error in column generation phase')
  else
      x.Type = 'integer';
      [values,logsUsed,exitflag] = solve(logprob,'options',ipopts);
      if double(exitflag) > 0
          values.x = round(values.x); % in case some values were not exactly integers
          logsUsed = sum(values.x);
          fprintf('Optimal solution uses %g logs\n', logsUsed);
          totalwaste = sum((patterns*values.x - quantity).*lengthlist); % waste due to overproduction
          for j = 1:size(values.x)
              if values.x(j) > 0
                  fprintf('Cut %g logs with pattern\n',values.x(j));
                  for w = 1:size(patterns,1)
                      if patterns(w,j) > 0
                          fprintf('    %g cut(s) of length %d\n', patterns(w,j),lengthlist(w));
                      end
                  end
                  wastej = logLength - dot(patterns(:,j),lengthlist); % waste due to pattern inefficiency
                  totalwaste = totalwaste + wastej;
              fprintf('    Waste of this pattern is %g\n',wastej);
              end
          end
          fprintf('Total waste in this problem is %g.\n',totalwaste);
      else 
          disp('Error in final optimization')
      end
  end

[Walter Roberson]

What release are you using?

[ziad]

R2017a mr.Roberson

Answer 1

[Alan Weiss]

As the release notes show, optimproblem was introduced in R2017b. Or you can find out this information at the bottom of the optimproblem function reference page.

Alan Weiss

MATLAB mathematical toolbox documentation