initial commit

LSAP.cpp

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+
+#include "AuctionAlgorithm/Auction.h"
+#include "AuctionAlgorithm/AuctionMT.h"
+#include "Matrix/SparseMatrix.h"
+#include <boost/random/mersenne_twister.hpp>
+#include <boost/random/uniform_int_distribution.hpp>
+#include <boost/random/uniform_real_distribution.hpp>
+using namespace LSAP;
+
+typedef double Scalar;
+
+typedef Eigen::Matrix<Scalar, -1, -1> WeightMatrix;
+
+int main(int argc, char **argv)
+{
+	const size_t rows = 2000, cols = 5000;
+
+	// assert that rows <= cols and coefficients are between 0 and 1!
+	Eigen::MatrixXd m = Eigen::MatrixXd::Random(rows, cols);
+
+	// shift coefficients to get positive values
+	for ( size_t i = 0; i < rows; ++i)
+		for ( size_t j = 0; j < cols; ++j )
+			m(i, j) += 1.;
+
+	m /= m.maxCoeff(); // normalize to 0..1
+
+	// create sparse matrix from dense
+	// this could take some time!
+	// currently there's an own sparse class, might be changed to eigen sparse type
+	// this matrix type stores the matrix in rowMajor AND colMajor format, so it uses
+	// a lot of space! (this was due to testing purposes)
+	SparseMatrix<double> s(m);
+
+	// result type
+	Edges solution;
+
+	// single threaded computation and some time measurement
+	MEASURE_DURATION_SINGLE((solution = Auction<double>::solve(m)));
+	MEASURE_DURATION_SINGLE((solution = Auction<double, SparseMatrix<double> >::solve(s)));
+
+	// multi threaded computation (2 threads) and time measurement
+	MEASURE_DURATION_SINGLE((solution = AuctionMT<double>::solve(m, 2)));
+	MEASURE_DURATION_SINGLE((solution = AuctionMT<double, SparseMatrix<double> >::solve(s, 2)));
+
+}