Accord.Math.Optimization.GaussNewton.Minimize C# (CSharp) Method

        public double Minimize(double[][] inputs, double[] outputs)
        {
            Array.Clear(hessian, 0, hessian.Length);
            Array.Clear(gradient, 0, gradient.Length);


            errors = new double[inputs.Length];
            jacobian = new double[inputs.Length, numberOfParameters];


            for (int i = 0; i < inputs.Length; i++)
                errors[i] = outputs[i] - Function(weights, inputs[i]);

            var g = new double[numberOfParameters];
            for (int i = 0; i < inputs.Length; i++)
            {
                Gradient(weights, inputs[i], result: g);

                for (int j = 0; j < gradient.Length; j++)
                    jacobian[i, j] = -g[j];

                if (Token.IsCancellationRequested)
                    break;
            }


            // Compute error gradient using Jacobian
            jacobian.TransposeAndDot(errors, result: gradient);

            // Compute Quasi-Hessian Matrix approximation
            jacobian.TransposeAndDot(jacobian, result: hessian);

            decomposition = new SingularValueDecomposition(hessian,
                computeLeftSingularVectors: true, computeRightSingularVectors: true, autoTranspose: true);

            deltas = decomposition.Solve(gradient);

            for (int i = 0; i < deltas.Length; i++)
                weights[i] -= deltas[i];

            return Value = ComputeError(inputs, outputs);
        }