Try our new documentation site (beta).


poolsearch.m


function poolsearch()

% Copyright 2018, Gurobi Optimization, LLC
%
% We find alternative epsilon-optimal solutions to a given knapsack
% problem by using PoolSearchMode

% define primitive data
groundSetSize = 10;
objCoef       = [32; 32; 15; 15; 6; 6; 1; 1; 1; 1];
knapsackCoef  = [16, 16,  8,  8, 4, 4, 2, 2, 1, 1];
Budget        = 33;

% Initialize model
model.modelsense  = 'max';
model.modelname   = 'poolsearch';

% Set variables
model.obj         = objCoef;
model.vtype       = repmat('B', groundSetSize, 1);
model.lb          = zeros(groundSetSize, 1);
model.ub          = ones(groundSetSize, 1);
for j = 1:groundSetSize
    model.varnames{j} = sprintf('El%d', j);
end

% Build constraint matrix
model.A           = sparse(knapsackCoef);
model.rhs         = Budget;
model.sense       = '<';
model.constrnames = {'Budget'};

% Set poolsearch parameters
params.PoolSolutions  = 1024;
params.PoolGap        = 0.10;
params.PoolSearchMode = 2;

% Save problem
gurobi_write(model, 'poolsearch_m.lp');

% Optimize
result = gurobi(model, params);

% Capture solution information
if ~strcmp(result.status, 'OPTIMAL')
    fprintf('Optimization finished with status %s, quit now\n', result.status);
    return;
end

% Print best solution
fprintf('Selected elements in best solution:\n');
for j = 1:groundSetSize
    if result.x(j) >= 0.9
        fprintf('%s ', model.varnames{j});
    end
end
fprintf('\n');

% Print all solution objectives and best furth solution
if isfield(result, 'pool') && ~isempty(result.pool)
    solcount = length(result.pool);
    fprintf('Number of solutions found: %d\n', solcount);
    fprintf('Objective values for all %d solutions:\n', solcount);
    for k = 1:solcount
        fprintf('%g ', result.pool(k).objval);
    end
    fprintf('\n');
    if solcount >= 4
        fprintf('Selected elements in fourth best solution:\n');
        for k = 1:groundSetSize
            if result.pool(4).xn(k) >= 0.9
                fprintf(' %s', model.varnames{k});
            end
        end
        fprintf('\n');
    end
else
    fprintf('Number of solutions found: 1\n');
    fprintf('Solution 1 has objective: %g \n', result.objval);
end

Try Gurobi for Free

Choose the evaluation license that fits you best, and start working with our Expert Team for technical guidance and support.

Evaluation License
Get a free, full-featured license of the Gurobi Optimizer to experience the performance, support, benchmarking and tuning services we provide as part of our product offering.
Academic License
Gurobi supports the teaching and use of optimization within academic institutions. We offer free, full-featured copies of Gurobi for use in class, and for research.
Cloud Trial

Request free trial hours, so you can see how quickly and easily a model can be solved on the cloud.

Search

Gurobi Optimization