//! minimize the optimization problem P public override EndCriteria.Type minimize(Problem P, EndCriteria endCriteria) { EndCriteria.Type ecType = EndCriteria.Type.None; P.reset(); Vector x_ = P.currentValue(); int iterationNumber_ = 0; int stationaryStateIterationNumber_ = 0; lineSearch_.searchDirection = new Vector(x_.Count); bool end; // function and squared norm of gradient values; double normdiff; // classical initial value for line-search step double t = 1.0; // Set gold at the size of the optimization problem search direction Vector gold = new Vector(lineSearch_.searchDirection.Count); Vector gdiff = new Vector(lineSearch_.searchDirection.Count); P.setFunctionValue(P.valueAndGradient(gold, x_)); lineSearch_.searchDirection = gold * -1.0; P.setGradientNormValue(Vector.DotProduct(gold, gold)); normdiff = Math.Sqrt(P.gradientNormValue()); do { // Linesearch t = lineSearch_.value(P, ref ecType, endCriteria, t); if (!(lineSearch_.succeed())) { throw new ApplicationException("line-search failed!"); } // End criteria // FIXME: it's never been used! ??? // , normdiff end = endCriteria.value(iterationNumber_, ref stationaryStateIterationNumber_, true, P.functionValue(), Math.Sqrt(P.gradientNormValue()), lineSearch_.lastFunctionValue(), Math.Sqrt(lineSearch_.lastGradientNorm2()), ref ecType); // Updates // New point x_ = lineSearch_.lastX(); // New function value P.setFunctionValue(lineSearch_.lastFunctionValue()); // New gradient and search direction vectors gdiff = gold - lineSearch_.lastGradient(); normdiff = Math.Sqrt(Vector.DotProduct(gdiff, gdiff)); gold = lineSearch_.lastGradient(); lineSearch_.searchDirection = gold * -1.0; // New gradient squared norm P.setGradientNormValue(lineSearch_.lastGradientNorm2()); // Increase interation number ++iterationNumber_; } while (end == false); P.setCurrentValue(x_); return(ecType); }
//! minimize the optimization problem P public override EndCriteria.Type minimize(Problem P, EndCriteria endCriteria) { EndCriteria.Type ecType = EndCriteria.Type.None; P.reset(); Vector x_ = P.currentValue(); int iterationNumber_ = 0; int stationaryStateIterationNumber_ = 0; lineSearch_.searchDirection = new Vector(x_.Count); bool end; // function and squared norm of gradient values; double normdiff; // classical initial value for line-search step double t = 1.0; // Set gold at the size of the optimization problem search direction Vector gold = new Vector(lineSearch_.searchDirection.Count); Vector gdiff = new Vector(lineSearch_.searchDirection.Count); P.setFunctionValue(P.valueAndGradient(gold, x_)); lineSearch_.searchDirection = gold*-1.0; P.setGradientNormValue(Vector.DotProduct(gold, gold)); normdiff = Math.Sqrt(P.gradientNormValue()); do { // Linesearch t = lineSearch_.value(P, ref ecType, endCriteria, t); if (!(lineSearch_.succeed())) throw new ApplicationException("line-search failed!"); // End criteria // FIXME: it's never been used! ??? // , normdiff end = endCriteria.value(iterationNumber_, ref stationaryStateIterationNumber_, true, P.functionValue(), Math.Sqrt(P.gradientNormValue()), lineSearch_.lastFunctionValue(), Math.Sqrt(lineSearch_.lastGradientNorm2()), ref ecType); // Updates // New point x_ = lineSearch_.lastX(); // New function value P.setFunctionValue(lineSearch_.lastFunctionValue()); // New gradient and search direction vectors gdiff = gold - lineSearch_.lastGradient(); normdiff = Math.Sqrt(Vector.DotProduct(gdiff, gdiff)); gold = lineSearch_.lastGradient(); lineSearch_.searchDirection = gold*-1.0; // New gradient squared norm P.setGradientNormValue(lineSearch_.lastGradientNorm2()); // Increase interation number ++iterationNumber_; } while (end == false); P.setCurrentValue(x_); return ecType; }