Added a converter from IObjectiveModel to IObjectiveFunction.

src/Numerics.Tests/OptimizationTests/NonLinearCurveFittingTests.cs

@@ -386,6 +386,19 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
             }
         }
 
+        [Test]
+        public void Bfgs_FindMinimum_BoxBod_Unconstrained()
+        {
+            var obj = ObjectiveModel.FittingFunction(BoxBodModel, BoxBodPrime, BoxBodX, BoxBodY);
+            var solver = new BfgsMinimizer(1e-10, 1e-10, 1e-10, 100);
+            var result = solver.FindMinimum(obj, BoxBodStart2);
+
+            for (int i = 0; i < result.MinimizingPoint.Count; i++)
+            {
+                AssertHelpers.AlmostEqualRelative(BoxBodPbest[i], result.MinimizingPoint[i], 6);
+            }
+        }
+
         // model : Thurber (https://www.itl.nist.gov/div898/strd/nls/data/thurber.shtml)
         //       f(x; b1 ... b7) = (b1 + b2*x + b3*x^2 + b4*x^3) / (1 + b5*x + b6*x^2 + b7*x^3) 
         // derivatives:
@@ -456,7 +469,9 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
         private Vector<double> ThurberPstd = new DenseVector(new double[] {
             4.6647963344E+00, 3.9571156086E+01, 2.8698696102E+01, 5.5675370270E+00, 3.1333340687E-02,
             1.4984928198E-02, 6.5842344623E-03 });
-        private Vector<double> ThurberInitialGuess = new DenseVector(new double[] { 1000.0, 1000.0, 400.0, 40.0, 0.7, 0.3, 0.03 });
+        private Vector<double> ThurberStart = new DenseVector(new double[] { 1000.0, 1000.0, 400.0, 40.0, 0.7, 0.3, 0.03 });
+        private Vector<double> ThurberLowerBound = new DenseVector(new double[] { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 });
+        private Vector<double> ThurberUpperBound = new DenseVector(new double[] { 1E6, 1E6, 1E6, 1E6, 1E6, 1E6, 1E6 });
         private Vector<double> ThurberScales = new DenseVector(new double[7] { 1000, 1000, 400, 40, 0.7, 0.3, 0.03 });
 
         [Test]
@@ -464,7 +479,7 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
         {
             var obj = ObjectiveModel.FittingModel(ThurberModel, ThurberPrime, ThurberX, ThurberY);
             var solver = new LevenbergMarquardtMinimizer();
-            var result = solver.FindMinimum(obj, ThurberInitialGuess);
+            var result = solver.FindMinimum(obj, ThurberStart);
 
             for (int i = 0; i < result.BestFitParameters.Count; i++)
             {
@@ -478,7 +493,7 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
         {
             var obj = ObjectiveModel.FittingModel(ThurberModel, ThurberX, ThurberY, accuracyOrder: 6);
             var solver = new LevenbergMarquardtMinimizer();
-            var result = solver.FindMinimum(obj, ThurberInitialGuess);
+            var result = solver.FindMinimum(obj, ThurberStart);
 
             for (int i = 0; i < result.BestFitParameters.Count; i++)
             {
@@ -494,7 +509,7 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
                 scales: ThurberScales,
                 accuracyOrder: 6);
             var solver = new TrustRegionDogLegMinimizer();
-            var result = solver.FindMinimum(obj, ThurberInitialGuess);
+            var result = solver.FindMinimum(obj, ThurberStart);
 
             for (int i = 0; i < result.BestFitParameters.Count; i++)
             {
@@ -510,7 +525,7 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
                 scales: ThurberScales,
                 accuracyOrder: 6);
             var solver = new TrustRegionNewtonCGMinimizer();
-            var result = solver.FindMinimum(obj, ThurberInitialGuess);
+            var result = solver.FindMinimum(obj, ThurberStart);
 
             for (int i = 0; i < result.BestFitParameters.Count; i++)
             {
@@ -518,5 +533,44 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
                 AssertHelpers.AlmostEqualRelative(ThurberPstd[i], result.StandardErrors[i], 3);
             }
         }
+
+        [Test]
+        public void Bfgs_FindMinimum_Thurber_Unconstrained()
+        {
+            var obj = ObjectiveModel.FittingFunction(ThurberModel, ThurberX, ThurberY, accuracyOrder: 6);
+            var solver = new BfgsMinimizer(1e-10, 1e-10, 1e-10, 1000);
+            var result = solver.FindMinimum(obj, ThurberStart);
+
+            for (int i = 0; i < result.MinimizingPoint.Count; i++)
+            {
+                AssertHelpers.AlmostEqualRelative(ThurberPbest[i], result.MinimizingPoint[i], 6);
+            }
+        }
+
+        [Test]
+        public void BfgsB_FindMinimum_Thurber()
+        {
+            var obj = ObjectiveModel.FittingFunction(ThurberModel, ThurberX, ThurberY, accuracyOrder: 6);
+            var solver = new BfgsBMinimizer(1e-10, 1e-10, 1e-10, 1000);
+            var result = solver.FindMinimum(obj, ThurberLowerBound, ThurberUpperBound, ThurberStart);
+
+            for (int i = 0; i < result.MinimizingPoint.Count; i++)
+            {
+                AssertHelpers.AlmostEqualRelative(ThurberPbest[i], result.MinimizingPoint[i], 6);
+            }
+        }
+
+        [Test]
+        public void LBfgs_FindMinimum_Thurber()
+        {
+            var obj = ObjectiveModel.FittingFunction(ThurberModel, ThurberX, ThurberY, accuracyOrder: 6);
+            var solver = new LimitedMemoryBfgsMinimizer(1e-10, 1e-10, 1e-10, 1000);
+            var result = solver.FindMinimum(obj, ThurberStart);
+
+            for (int i = 0; i < result.MinimizingPoint.Count; i++)
+            {
+                AssertHelpers.AlmostEqualRelative(ThurberPbest[i], result.MinimizingPoint[i], 6);
+            }
+        }
     }
 }

src/Numerics/Optimization/IObjectiveModel.cs

@@ -66,5 +66,7 @@ namespace MathNet.Numerics.Optimization
 
         /// <summary>Create a new independent copy of this objective function, evaluated at the same point.</summary>
         IObjectiveModel Fork();
+
+        IObjectiveFunction ToObjectiveFunction();
     }
 }

src/Numerics/Optimization/ObjectiveModel.cs

@@ -36,5 +36,29 @@ namespace MathNet.Numerics.Optimization
             objective.SetParameters(lowerBound, upperBound, scales, isFixed);
             return objective;
         }
+
+        /// <summary>
+        /// Fitting function with a user supplied jacobian for nonlinear least squares regression by the line search algorithm.
+        /// </summary>
+        public static IObjectiveFunction FittingFunction(Func<Vector<double>, double, double> function, Func<Vector<double>, double, Vector<double>> derivatives,
+            Vector<double> observedX, Vector<double> observedY, Vector<double> weight = null)
+        {
+            var objective = new FittingObjectiveModel(function, derivatives);
+            objective.SetObserved(observedX, observedY, weight);
+            return objective.ToObjectiveFunction();
+        }
+
+        /// <summary>
+        /// Fitting function for nonlinear least squares regression by the line search algorithm.
+        /// The numerical jacobian with accuracy order is used.
+        /// </summary>
+        public static IObjectiveFunction FittingFunction(Func<Vector<double>, double, double> function,
+            Vector<double> observedX, Vector<double> observedY, Vector<double> weight = null,
+            int accuracyOrder = 2)
+        {
+            var objective = new FittingObjectiveModel(function, null, accuracyOrder: accuracyOrder);
+            objective.SetObserved(observedX, observedY, weight);
+            return objective.ToObjectiveFunction();
+        }
     }
 }

src/Numerics/Optimization/ObjectiveModels/FittingObjectiveModel.cs

@@ -1,4 +1,5 @@
 using MathNet.Numerics.LinearAlgebra;
+using MathNet.Numerics.Optimization.ObjectiveFunctions;
 using System;
 using System.Collections.Generic;
 using System.Linq;
@@ -172,7 +173,27 @@ namespace MathNet.Numerics.Optimization.ObjectiveModels
 
         public IObjectiveModel CreateNew()
         {
-            return new FittingObjectiveModel(userFunction, userDerivatives);
+            return new FittingObjectiveModel(userFunction, userDerivatives, AccuracyOrder);
+        }
+
+        public IObjectiveFunction ToObjectiveFunction()
+        {
+            Tuple<double, Vector<double>, Matrix<double>> function(Vector<double> point)
+            {
+                EvaluateFunction(point);
+                EvaluateJacobian(point);
+
+                return new Tuple<double, Vector<double>, Matrix<double>>(Residue, -Gradient, Hessian);
+            }
+
+            LowerBound = null;
+            UpperBound = null;
+            Scales = null;
+            IsFixed = null;
+            IsBounded = false;
+
+            var objective = new GradientHessianObjectiveFunction(function);
+            return objective;
         }
 
         /// <summary>