Ejemplo n.º 1
0
        /// <summary>
        /// Element (i, j) is the df_i/dx_j. i.e it is the partial derivative of the ith element of f with respect to the jth input.
        /// </summary>
        /// <param name="jacobian"></param>
        /// <param name="objective"></param>
        private void UpdateJacobian(Matrix <double> jacobian, IObjectiveVectorFunction objective)
        {
            var baseValues = objective.Value.Clone();
            var point      = objective.Point;

            for (var col = 0; col < jacobian.ColumnCount; col++)
            {
                point[col] += shift;
                objective.EvaluateAt(point);
                var allZero = true;
                for (var row = 0; row < jacobian.RowCount; row++)
                {
                    jacobian[row, col] = (objective.Value[row] - baseValues[row]) / shift;
                    if (allZero && Math.Abs(jacobian[row, col]) > 1e-12)
                    {
                        allZero = false;
                    }
                }
                if (allZero)
                {
                    throw new ArgumentException($"instrument at position {col} has no sensitivity to any inputs.");
                }
                point[col] -= shift;
            }
        }
Ejemplo n.º 2
0
        public VectorMinimizationResult FindRoot(IObjectiveVectorFunction objective, Vector <double> initialGuess)
        {
            var jacobian = Matrix <double> .Build.Dense(initialGuess.Count, initialGuess.Count);

            var guess = initialGuess.Clone();
            int iterCount;

            for (iterCount = 0; iterCount < _maximumIterations; iterCount++)
            {
                objective.EvaluateAt(guess);
                var baseValues = objective.Value.Clone();
                if (baseValues.AbsoluteMaximum() < _convergenceTolerance)
                {
                    break;
                }
                UpdateJacobian(jacobian, objective);
                var inverse = jacobian.Inverse();
                guess = guess - inverse * baseValues;
            }

            var result = new VectorMinimizationResult
            {
                FunctionInfoAtMinimum = objective,
                Iterations            = iterCount,
                MinimizingPoint       = guess,
                ReasonForExit         = ExitCondition.Converged
            };

            return(result);
        }
        public VectorMinimizationResult FindRoot(IObjectiveVectorFunction objective, Vector <double> initialGuess)
        {
            var function = new FunctionEvaluator(objective);
            var root     = Broyden.FindRoot(function.Eval, initialGuess.ToArray(), 1e-8, 100, 1e-8);
            var result   = new VectorMinimizationResult
            {
                FunctionInfoAtMinimum = objective,
                Iterations            = -1,
                MinimizingPoint       = new DenseVector(root),
                ReasonForExit         = ExitCondition.Converged
            };

            return(result);
        }
Ejemplo n.º 4
0
 internal FunctionEvaluator(IObjectiveVectorFunction objective)
 {
     _objective = objective;
 }