public override EndCriteria.Type minimize(Problem P, EndCriteria endCriteria) { EndCriteria.Type ecType = EndCriteria.Type.None; P.reset(); Vector x_ = P.currentValue(); currentProblem_ = P; initCostValues_ = P.costFunction().values(x_); int m = initCostValues_.size(); int n = x_.size(); if (useCostFunctionsJacobian_) { initJacobian_ = new Matrix(m, n); P.costFunction().jacobian(initJacobian_, x_); } Vector xx = new Vector(x_); Vector fvec = new Vector(m), diag = new Vector(n); int mode = 1; double factor = 1; int nprint = 0; int info = 0; int nfev = 0; Matrix fjac = new Matrix(m, n); int ldfjac = m; List <int> ipvt = new InitializedList <int>(n); Vector qtf = new Vector(n), wa1 = new Vector(n), wa2 = new Vector(n), wa3 = new Vector(n), wa4 = new Vector(m); // call lmdif to minimize the sum of the squares of m functions // in n variables by the Levenberg-Marquardt algorithm. Func <int, int, Vector, int, Matrix> j = null; if (useCostFunctionsJacobian_) { j = jacFcn; } // requirements; check here to get more detailed error messages. Utils.QL_REQUIRE(n > 0, () => "no variables given"); Utils.QL_REQUIRE(m >= n, () => $"less functions ({m}) than available variables ({n})"); Utils.QL_REQUIRE(endCriteria.functionEpsilon() >= 0.0, () => "negative f tolerance"); Utils.QL_REQUIRE(xtol_ >= 0.0, () => "negative x tolerance"); Utils.QL_REQUIRE(gtol_ >= 0.0, () => "negative g tolerance"); Utils.QL_REQUIRE(endCriteria.maxIterations() > 0, () => "null number of evaluations"); MINPACK.lmdif(m, n, xx, ref fvec, endCriteria.functionEpsilon(), xtol_, gtol_, endCriteria.maxIterations(), epsfcn_, diag, mode, factor, nprint, ref info, ref nfev, ref fjac, ldfjac, ref ipvt, ref qtf, wa1, wa2, wa3, wa4, fcn, j); info_ = info; // check requirements & endCriteria evaluation Utils.QL_REQUIRE(info != 0, () => "MINPACK: improper input parameters"); if (info != 6) { ecType = EndCriteria.Type.StationaryFunctionValue; } endCriteria.checkMaxIterations(nfev, ref ecType); Utils.QL_REQUIRE(info != 7, () => "MINPACK: xtol is too small. no further " + "improvement in the approximate " + "solution x is possible."); Utils.QL_REQUIRE(info != 8, () => "MINPACK: gtol is too small. fvec is " + "orthogonal to the columns of the " + "jacobian to machine precision."); // set problem x_ = new Vector(xx.GetRange(0, n)); P.setCurrentValue(x_); P.setFunctionValue(P.costFunction().value(x_)); return(ecType); }