Changed the sign of the gradient of the FittingObjectiveModel to match the scheme of the existing Minimizers.

src/Numerics/Optimization/LevenbergMarquardtMinimizer.cs

@@ -92,14 +92,14 @@ namespace MathNet.Numerics.Optimization
             //    Residuals, R = L(y - f(x; p))
             //    Residual sum of squares, RSS = ||R||^2 = R.DotProduct(R)
             //    Jacobian J = df(x; p)/dp
-            //    Gradient g = J'W(y − f(x; p)) = J'LR 
+            //    Gradient g = -J'W(y − f(x; p)) = -J'LR 
             //    Approximated Hessian H = J'WJ
             //
             // The Levenberg-Marquardt algorithm is summarized as follows:
-            //    initially let μ = τ * max(diag(J'WJ)).
+            //    initially let μ = τ * max(diag(H)).
             //    repeat 
-            //       solve linear equations: (J'WJ + μI)ΔP = J'R
-            //       let ρ = (||R||^2 - ||Rnew||^2) / (Δp'(μΔp + J'R)).
+            //       solve linear equations: (H + μI)ΔP = -g
+            //       let ρ = (||R||^2 - ||Rnew||^2) / (Δp'(μΔp - g)).
             //       if ρ > ε, P = P + ΔP; μ = μ * max(1/3, 1 - (2ρ - 1)^3); ν = 2;
             //       otherwise μ = μ*ν; ν = 2*ν;
             //
@@ -186,7 +186,7 @@ namespace MathNet.Numerics.Optimization
                     Hessian.SetDiagonal(Hessian.Diagonal() + mu); // hessian[i, i] = hessian[i, i] + mu;
                     
                     // solve normal equations
-                    Pstep = Hessian.Solve(Gradient);
+                    Pstep = Hessian.Solve(-Gradient);
 
                     // if ||ΔP|| <= xTol * (||P|| + xTol), found and stop
                     if (Pstep.L2Norm() <= stepTolerance * (stepTolerance + P.DotProduct(P)))
@@ -207,8 +207,8 @@ namespace MathNet.Numerics.Optimization
                     }
 
                     // calculate the ratio of the actual to the predicted reduction.
-                    // ρ = (RSS - RSSnew) / (Δp'(μΔp + g))
-                    var predictedReduction = Pstep.DotProduct(mu * Pstep + Gradient);
+                    // ρ = (RSS - RSSnew) / (Δp'(μΔp - g))
+                    var predictedReduction = Pstep.DotProduct(mu * Pstep - Gradient);
                     var rho = (predictedReduction != 0)
                             ? (RSS - RSSnew) / predictedReduction
                             : 0;

src/Numerics/Optimization/ObjectiveModels/FittingObjectiveModel.cs

@@ -188,7 +188,7 @@ namespace MathNet.Numerics.Optimization.ObjectiveModels
                 EvaluateFunction(point);
                 EvaluateJacobian(point);
 
-                return new Tuple<double, Vector<double>, Matrix<double>>(Residue, -Gradient, Hessian);
+                return new Tuple<double, Vector<double>, Matrix<double>>(Residue, Gradient, Hessian);
             }
 
             LowerBound = null;
@@ -465,10 +465,10 @@ namespace MathNet.Numerics.Optimization.ObjectiveModels
                 }
             }
 
-            // Gradient, g = J'W(y − f(x; p)) = J'L(L'E) = J'LR
+            // Gradient, g = -J'W(y − f(x; p)) = -J'L(L'E) = -J'LR
             Gradient = (Weights == null)
-                ? Jacobian.Transpose() * (ObservedY - Values)
-                : Jacobian.Transpose() * Weights * (ObservedY - Values);
+                ? -Jacobian.Transpose() * (ObservedY - Values)
+                : -Jacobian.Transpose() * Weights * (ObservedY - Values);
 
             // approximated Hessian, H = J'WJ + ∑LRiHi ~ J'WJ near the minimum
             Hessian = (Weights == null)

src/Numerics/Optimization/Subproblems/DogLegSubproblem.cs

@@ -15,12 +15,12 @@ namespace MathNet.Numerics.Optimization.Subproblems
 
             // newton point
             // the Gauss–Newton step by solving the normal equations
-            var Pgn = Hessian.PseudoInverse() * Gradient; // Hessian.Solve(Gradient) fails so many times...
+            var Pgn = -Hessian.PseudoInverse() * Gradient; // Hessian.Solve(Gradient) fails so many times...
 
             // cauchy point
             // steepest descent direction is given by
             var alpha = Gradient.DotProduct(Gradient) / (Hessian * Gradient).DotProduct(Gradient);
-            var Psd = alpha * Gradient;
+            var Psd = -alpha * Gradient;
 
             // update step and prectted reduction
             if (Pgn.L2Norm() <= delta)

src/Numerics/Optimization/Subproblems/NewtonCGSubproblem.cs

@@ -15,11 +15,12 @@ namespace MathNet.Numerics.Optimization.Subproblems
             var Hessian = objective.Hessian;
 
             // define tolerance
-            var tolerance = Math.Min(0.5, Math.Sqrt(Gradient.L2Norm())) * Gradient.L2Norm();
+            var gnorm = Gradient.L2Norm();
+            var tolerance = Math.Min(0.5, Math.Sqrt(gnorm)) * gnorm;
 
             // initialize internal variables
             var z = Vector<double>.Build.Dense(Hessian.RowCount);
-            var r = -Gradient;
+            var r = Gradient;
             var d = -r;
 
             while (true)

src/Numerics/Optimization/TrustRegionMinimizerBase.cs

@@ -93,7 +93,7 @@ namespace MathNet.Numerics.Optimization
             //    Residuals, R = L(y - f(x; p))
             //    Residual sum of squares, RSS = ||R||^2 = R.DotProduct(R)
             //    Jacobian J = df(x; p)/dp
-            //    Gradient g = J'W(y − f(x; p)) = J'LR
+            //    Gradient g = -J'W(y − f(x; p)) = -J'LR
             //    Approximated Hessian H = J'WJ
             //
             // The trust region algorithm is summarized as follows:
@@ -190,8 +190,8 @@ namespace MathNet.Numerics.Optimization
                 subproblem.Solve(objective, delta);
                 var Pstep = subproblem.Pstep;
                 var hitBoundary = subproblem.HitBoundary;
-                // predicted reduction = L(0) - L(Δp) = Δp'g - 1/2 * Δp'HΔp
-                var predictedReduction = objective.Gradient.DotProduct(Pstep) - 0.5 * Pstep.DotProduct(objective.Hessian * Pstep);
+                // predicted reduction = L(0) - L(Δp) = -Δp'g - 1/2 * Δp'HΔp
+                var predictedReduction = -objective.Gradient.DotProduct(Pstep) - 0.5 * Pstep.DotProduct(objective.Hessian * Pstep);
 
                 if (Pstep.L2Norm() <= stepTolerance * (stepTolerance + P.L2Norm()))
                 {