Optimization: rename Evaluation to ObjectiveFunction

src/Numerics/Numerics.csproj

@@ -116,10 +116,11 @@
     <Compile Include="OdeSolvers\AdamsBashforth.cs" />
     <Compile Include="OdeSolvers\RungeKutta.cs" />
     <Compile Include="Optimization\BaseEvaluation.cs" />
+    <Compile Include="Optimization\BaseObjectiveFunction.cs" />
     <Compile Include="Optimization\Exceptions.cs" />
-    <Compile Include="Optimization\IEvaluation.cs" />
+    <Compile Include="Optimization\IObjectiveFunction.cs" />
     <Compile Include="Optimization\Implementation\LineSearchOutput.cs" />
-    <Compile Include="Optimization\Implementation\NullEvaluation.cs" />
+    <Compile Include="Optimization\Implementation\NullObjectiveFunction.cs" />
     <Compile Include="Optimization\Implementation\ObjectiveChecker.cs" />
     <Compile Include="Optimization\Implementation\WeakWolfeLineSearch.cs" />
     <Compile Include="Optimization\IUnconstrainedMinimizer.cs" />

src/Numerics/Optimization/BaseEvaluation.cs → src/Numerics/Optimization/BaseObjectiveFunction.cs

@@ -2,7 +2,7 @@
 
 namespace MathNet.Numerics.Optimization
 {
-    public abstract class BaseEvaluation : IEvaluation
+    public abstract class BaseObjectiveFunction : IObjectiveFunction
     {
         public EvaluationStatus Status { get; protected set; }
 
@@ -14,7 +14,7 @@ namespace MathNet.Numerics.Optimization
         public bool GradientSupported { get; private set; }
         public bool HessianSupported { get; private set; }
 
-        protected BaseEvaluation(bool gradientSupported, bool hessianSupported)
+        protected BaseObjectiveFunction(bool gradientSupported, bool hessianSupported)
         {
             Status = EvaluationStatus.None;
             GradientSupported = gradientSupported;
@@ -74,6 +74,6 @@ namespace MathNet.Numerics.Optimization
         protected abstract void SetValue();
         protected abstract void SetGradient();
         protected abstract void SetHessian();
-        public abstract IEvaluation CreateNew();
+        public abstract IObjectiveFunction CreateNew();
     }
 }

src/Numerics/Optimization/Exceptions.cs

@@ -19,18 +19,18 @@ namespace MathNet.Numerics.Optimization
 
     public class EvaluationException : OptimizationException
     {
-        public IEvaluation Evaluation { get; private set; }
+        public IObjectiveFunction ObjectiveFunction { get; private set; }
 
-        public EvaluationException(string message, IEvaluation eval)
+        public EvaluationException(string message, IObjectiveFunction eval)
             : base(message)
         {
-            this.Evaluation = eval;
+            this.ObjectiveFunction = eval;
         }
 
-        public EvaluationException(string message, IEvaluation eval, Exception inner_exception)
+        public EvaluationException(string message, IObjectiveFunction eval, Exception inner_exception)
             : base(message, inner_exception)
         {
-            this.Evaluation = eval;
+            this.ObjectiveFunction = eval;
         }
 
         //public EvaluationException(string message, IEvaluation1D eval)

src/Numerics/Optimization/IEvaluation.cs → src/Numerics/Optimization/IObjectiveFunction.cs

@@ -6,10 +6,10 @@ namespace MathNet.Numerics.Optimization
     [Flags]
     public enum EvaluationStatus { None = 0, Value = 1, Gradient = 2, Hessian = 4 }
 
-    public interface IEvaluation
+    public interface IObjectiveFunction
     {
         Vector<double> Point { get; set; }
-        IEvaluation CreateNew();
+        IObjectiveFunction CreateNew();
 
         // Used by algorithm
         bool GradientSupported { get; }

src/Numerics/Optimization/IUnconstrainedMinimizer.cs

@@ -4,6 +4,6 @@ namespace MathNet.Numerics.Optimization
 {
     public interface IUnconstrainedMinimizer
     {
-        MinimizationOutput FindMinimum(IEvaluation objective, Vector<double> initialGuess);
+        MinimizationOutput FindMinimum(IObjectiveFunction objective, Vector<double> initialGuess);
     }
 }

src/Numerics/Optimization/Implementation/LineSearchOutput.cs

@@ -4,7 +4,7 @@
     {
         public double FinalStep { get; private set; }
 
-        public LineSearchOutput(IEvaluation functionInfo, int iterations, double finalStep, ExitCondition reasonForExit)
+        public LineSearchOutput(IObjectiveFunction functionInfo, int iterations, double finalStep, ExitCondition reasonForExit)
             : base(functionInfo, iterations, reasonForExit)
         {
             FinalStep = finalStep;

src/Numerics/Optimization/Implementation/NullEvaluation.cs → src/Numerics/Optimization/Implementation/NullObjectiveFunction.cs

@@ -3,9 +3,9 @@ using MathNet.Numerics.LinearAlgebra;
 
 namespace MathNet.Numerics.Optimization.Implementation
 {
-    public class NullEvaluation : BaseEvaluation
+    public class NullObjectiveFunction : BaseObjectiveFunction
     {
-        public NullEvaluation(Vector<double> point)
+        public NullObjectiveFunction(Vector<double> point)
             : base(false, false)
         {
             Point = point;
@@ -25,7 +25,7 @@ namespace MathNet.Numerics.Optimization.Implementation
             throw new NotImplementedException();
         }
 
-        public override IEvaluation CreateNew()
+        public override IObjectiveFunction CreateNew()
         {
             throw new NotImplementedException();
         }

src/Numerics/Optimization/Implementation/ObjectiveChecker.cs

@@ -3,20 +3,20 @@ using MathNet.Numerics.LinearAlgebra;
 
 namespace MathNet.Numerics.Optimization.Implementation
 {
-    public class CheckedEvaluation : IEvaluation
+    public class CheckedObjectiveFunction : IObjectiveFunction
     {
         private bool _valueChecked;
         private bool _gradientChecked;
         private bool _hessianChecked;
 
-        public IEvaluation InnerEvaluation { get; private set; }
+        public IObjectiveFunction InnerObjectiveFunction { get; private set; }
-        public Action<IEvaluation> ValueChecker { get; private set; }
+        public Action<IObjectiveFunction> ValueChecker { get; private set; }
-        public Action<IEvaluation> GradientChecker { get; private set; }
+        public Action<IObjectiveFunction> GradientChecker { get; private set; }
-        public Action<IEvaluation> HessianChecker { get; private set; }
+        public Action<IObjectiveFunction> HessianChecker { get; private set; }
 
-        public CheckedEvaluation(IEvaluation objective, Action<IEvaluation> valueChecker, Action<IEvaluation> gradientChecker, Action<IEvaluation> hessianChecker)
+        public CheckedObjectiveFunction(IObjectiveFunction objective, Action<IObjectiveFunction> valueChecker, Action<IObjectiveFunction> gradientChecker, Action<IObjectiveFunction> hessianChecker)
         {
-            InnerEvaluation = objective;
+            InnerObjectiveFunction = objective;
             ValueChecker = valueChecker;
             GradientChecker = gradientChecker;
             HessianChecker = hessianChecker;
@@ -24,8 +24,8 @@ namespace MathNet.Numerics.Optimization.Implementation
 
         public Vector<double> Point
         {
-            get { return InnerEvaluation.Point; }
+            get { return InnerObjectiveFunction.Point; }
-            set { InnerEvaluation.Point = value; }
+            set { InnerObjectiveFunction.Point = value; }
         }
 
         public double Value
@@ -38,16 +38,16 @@ namespace MathNet.Numerics.Optimization.Implementation
                     double tmp;
                     try
                     {
-                        tmp = InnerEvaluation.Value;
+                        tmp = InnerObjectiveFunction.Value;
                     }
                     catch (Exception e)
                     {
-                        throw new EvaluationException("Objective function evaluation failed.", InnerEvaluation, e);
+                        throw new EvaluationException("Objective function evaluation failed.", InnerObjectiveFunction, e);
                     }
-                    ValueChecker(InnerEvaluation);
+                    ValueChecker(InnerObjectiveFunction);
                     _valueChecked = true;
                 }
-                return InnerEvaluation.Value;
+                return InnerObjectiveFunction.Value;
             }
         }
 
@@ -61,16 +61,16 @@ namespace MathNet.Numerics.Optimization.Implementation
                     Vector<double> tmp;
                     try
                     {
-                        tmp = InnerEvaluation.Gradient;
+                        tmp = InnerObjectiveFunction.Gradient;
                     }
                     catch (Exception e)
                     {
-                        throw new EvaluationException("Objective gradient evaluation failed.", InnerEvaluation, e);
+                        throw new EvaluationException("Objective gradient evaluation failed.", InnerObjectiveFunction, e);
                     }
-                    GradientChecker(InnerEvaluation);
+                    GradientChecker(InnerObjectiveFunction);
                     _gradientChecked = true;
                 }
-                return InnerEvaluation.Gradient;
+                return InnerObjectiveFunction.Gradient;
             }
         }
 
@@ -84,32 +84,32 @@ namespace MathNet.Numerics.Optimization.Implementation
                     Matrix<double> tmp;
                     try
                     {
-                        tmp = InnerEvaluation.Hessian;
+                        tmp = InnerObjectiveFunction.Hessian;
                     }
                     catch (Exception e)
                     {
-                        throw new EvaluationException("Objective hessian evaluation failed.", InnerEvaluation, e);
+                        throw new EvaluationException("Objective hessian evaluation failed.", InnerObjectiveFunction, e);
                     }
-                    HessianChecker(InnerEvaluation);
+                    HessianChecker(InnerObjectiveFunction);
                     _hessianChecked = true;
                 }
-                return InnerEvaluation.Hessian;
+                return InnerObjectiveFunction.Hessian;
             }
         }
 
-        public IEvaluation CreateNew()
+        public IObjectiveFunction CreateNew()
         {
-            return new CheckedEvaluation(InnerEvaluation, ValueChecker, GradientChecker, HessianChecker);
+            return new CheckedObjectiveFunction(InnerObjectiveFunction, ValueChecker, GradientChecker, HessianChecker);
         }
 
         public bool GradientSupported
         {
-            get { return InnerEvaluation.GradientSupported; }
+            get { return InnerObjectiveFunction.GradientSupported; }
         }
 
         public bool HessianSupported
         {
-            get { return InnerEvaluation.HessianSupported; }
+            get { return InnerObjectiveFunction.HessianSupported; }
         }
     }
 }

src/Numerics/Optimization/Implementation/WeakWolfeLineSearch.cs

@@ -19,11 +19,11 @@ namespace MathNet.Numerics.Optimization.Implementation
         }
 
         // Implemented following http://www.math.washington.edu/~burke/crs/408/lectures/L9-weak-Wolfe.pdf
-        public LineSearchOutput FindConformingStep(IEvaluation objective, IEvaluation startingPoint, Vector<double> searchDirection, double initialStep)
+        public LineSearchOutput FindConformingStep(IObjectiveFunction objective, IObjectiveFunction startingPoint, Vector<double> searchDirection, double initialStep)
         {
-            if (!(objective is CheckedEvaluation))
+            if (!(objective is CheckedObjectiveFunction))
             {
-                objective = new CheckedEvaluation(objective, ValidateValue, ValidateGradient, null);
+                objective = new CheckedObjectiveFunction(objective, ValidateValue, ValidateGradient, null);
             }
 
             double lowerBound = 0.0;
@@ -36,7 +36,7 @@ namespace MathNet.Numerics.Optimization.Implementation
             double initialDd = searchDirection * initialGradient;
 
             int ii;
-            IEvaluation candidateEval = objective.CreateNew();
+            IObjectiveFunction candidateEval = objective.CreateNew();
             MinimizationOutput.ExitCondition reasonForExit = MinimizationOutput.ExitCondition.None;
             for (ii = 0; ii < _maximumIterations; ++ii)
             {
@@ -89,7 +89,7 @@ namespace MathNet.Numerics.Optimization.Implementation
             return new LineSearchOutput(candidateEval, ii, step, reasonForExit);
         }
 
-        bool Conforms(IEvaluation startingPoint, Vector<double> searchDirection, double step, IEvaluation endingPoint)
+        bool Conforms(IObjectiveFunction startingPoint, Vector<double> searchDirection, double step, IObjectiveFunction endingPoint)
         {
             bool sufficientDecrease = endingPoint.Value <= startingPoint.Value + _c1 * step * (startingPoint.Gradient * searchDirection);
             bool notTooSteep = endingPoint.Gradient * searchDirection >= _c2 * startingPoint.Gradient * searchDirection;
@@ -97,7 +97,7 @@ namespace MathNet.Numerics.Optimization.Implementation
             return step > 0 && sufficientDecrease && notTooSteep;
         }
 
-        void ValidateValue(IEvaluation eval)
+        void ValidateValue(IObjectiveFunction eval)
         {
             if (!IsFinite(eval.Value))
             {
@@ -105,7 +105,7 @@ namespace MathNet.Numerics.Optimization.Implementation
             }
         }
 
-        void ValidateGradient(IEvaluation eval)
+        void ValidateGradient(IObjectiveFunction eval)
         {
             foreach (double x in eval.Gradient)
             {

src/Numerics/Optimization/MinimizationOutput.cs

@@ -7,11 +7,11 @@ namespace MathNet.Numerics.Optimization
         public enum ExitCondition { None, RelativeGradient, LackOfProgress, AbsoluteGradient, WeakWolfeCriteria, BoundTolerance, StrongWolfeCriteria, LackOfFunctionImprovement }
 
         public Vector<double> MinimizingPoint { get { return FunctionInfoAtMinimum.Point; } }
-        public IEvaluation FunctionInfoAtMinimum { get; private set; }
+        public IObjectiveFunction FunctionInfoAtMinimum { get; private set; }
         public int Iterations { get; private set; }
         public ExitCondition ReasonForExit { get; private set; }
 
-        public MinimizationOutput(IEvaluation functionInfo, int iterations, ExitCondition reasonForExit)
+        public MinimizationOutput(IObjectiveFunction functionInfo, int iterations, ExitCondition reasonForExit)
         {
             FunctionInfoAtMinimum = functionInfo;
             Iterations = iterations;

src/Numerics/Optimization/MinimizationWithLineSearchOutput.cs

@@ -5,7 +5,7 @@
         public int TotalLineSearchIterations { get; private set; }
         public int IterationsWithNonTrivialLineSearch { get; private set; }
 
-        public MinimizationWithLineSearchOutput(IEvaluation functionInfo, int iterations, ExitCondition reasonForExit, int totalLineSearchIterations, int iterationsWithNonTrivialLineSearch)
+        public MinimizationWithLineSearchOutput(IObjectiveFunction functionInfo, int iterations, ExitCondition reasonForExit, int totalLineSearchIterations, int iterationsWithNonTrivialLineSearch)
             : base(functionInfo, iterations, reasonForExit)
         {
             TotalLineSearchIterations = totalLineSearchIterations;

src/Numerics/Optimization/NewtonMinimizer.cs

@@ -18,7 +18,7 @@ namespace MathNet.Numerics.Optimization
             UseLineSearch = useLineSearch;
         }
 
-        public MinimizationOutput FindMinimum(IEvaluation objective, Vector<double> initialGuess)
+        public MinimizationOutput FindMinimum(IObjectiveFunction objective, Vector<double> initialGuess)
         {
             if (!objective.GradientSupported)
                 throw new IncompatibleObjectiveException("Gradient not supported in objective function, but required for Newton minimization.");
@@ -26,10 +26,10 @@ namespace MathNet.Numerics.Optimization
             if (!objective.HessianSupported)
                 throw new IncompatibleObjectiveException("Hessian not supported in objective function, but required for Newton minimization.");
 
-            if (!(objective is CheckedEvaluation))
+            if (!(objective is CheckedObjectiveFunction))
-                objective = new CheckedEvaluation(objective, ValidateObjective, ValidateGradient, ValidateHessian);
+                objective = new CheckedObjectiveFunction(objective, ValidateObjective, ValidateGradient, ValidateHessian);
 
-            IEvaluation initialEval = objective.CreateNew();
+            IObjectiveFunction initialEval = objective.CreateNew();
             initialEval.Point = initialGuess;
 
             // Check that we're not already done
@@ -40,7 +40,7 @@ namespace MathNet.Numerics.Optimization
             var lineSearcher = new WeakWolfeLineSearch(1e-4, 0.9, 1e-4, maxIterations: 1000);
 
             // Declare state variables
-            IEvaluation candidatePoint = initialEval;
+            IObjectiveFunction candidatePoint = initialEval;
 
             // Subsequent steps
             int iterations = 0;
@@ -92,7 +92,7 @@ namespace MathNet.Numerics.Optimization
             return gradient.Norm(2.0) < GradientTolerance;
         }
 
-        private void ValidateGradient(IEvaluation eval)
+        private void ValidateGradient(IObjectiveFunction eval)
         {
             foreach (var x in eval.Gradient)
             {
@@ -101,13 +101,13 @@ namespace MathNet.Numerics.Optimization
             }
         }
 
-        private void ValidateObjective(IEvaluation eval)
+        private void ValidateObjective(IObjectiveFunction eval)
         {
             if (Double.IsNaN(eval.Value) || Double.IsInfinity(eval.Value))
                 throw new EvaluationException("Non-finite objective function returned.", eval);
         }
 
-        private void ValidateHessian(IEvaluation eval)
+        private void ValidateHessian(IObjectiveFunction eval)
         {
             for (int ii = 0; ii < eval.Hessian.RowCount; ++ii)
             {

src/UnitTests/OptimizationTests/RosenbrockFunction.cs

@@ -1,9 +1,6 @@
 using System;
-using System.Collections.Generic;
-using System.Linq;
-using System.Text;
-using System.Threading.Tasks;
 using MathNet.Numerics.LinearAlgebra;
+using MathNet.Numerics.LinearAlgebra.Double;
 
 namespace MathNet.Numerics.UnitTests.OptimizationTests
 {
@@ -16,7 +13,7 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
 
         public static Vector<double> Gradient(Vector<double> input)
         {
-            Vector<double> output = new MathNet.Numerics.LinearAlgebra.Double.DenseVector(2);
+            Vector<double> output = new DenseVector(2);
             output[0] = -2 * (1 - input[0]) + 200 * (input[1] - input[0] * input[0]) * (-2 * input[0]);
             output[1] = 2 * 100 * (input[1] - input[0] * input[0]);
             return output;
@@ -24,8 +21,7 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
 
         public static Matrix<double> Hessian(Vector<double> input)
         {
-
+            Matrix<double> output = new DenseMatrix(2, 2);
-            Matrix<double> output = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(2, 2);
             output[0, 0] = 2 - 400 * input[1] + 1200 * input[0] * input[0];
             output[1, 1] = 200;
             output[0, 1] = -400 * input[0];
@@ -50,6 +46,5 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
         {
             return 100.0 * RosenbrockFunction.Hessian(input / 100.0);
         }
-
     }
 }

src/UnitTests/OptimizationTests/TestNewtonMinimizer.cs

@@ -1,37 +1,33 @@
 using System;
-using System.Collections.Generic;
+using MathNet.Numerics.LinearAlgebra.Double;
-using System.Linq;
-using System.Text;
-using System.Threading.Tasks;
-
-using NUnit.Framework;
 using MathNet.Numerics.Optimization;
+using NUnit.Framework;
 
 namespace MathNet.Numerics.UnitTests.OptimizationTests
 {
-    public class RosenbrockEvaluation : BaseEvaluation
+    public class RosenbrockObjectiveFunction : BaseObjectiveFunction
     {
-        public RosenbrockEvaluation()
+        public RosenbrockObjectiveFunction()
             : base(true, true) { }
 
         protected override void SetValue()
         {
-            this.ValueRaw = RosenbrockFunction.Value(this.Point);
+            ValueRaw = RosenbrockFunction.Value(Point);
         }
 
         protected override void SetGradient()
         {
-            this.GradientRaw = RosenbrockFunction.Gradient(this.Point);
+            GradientRaw = RosenbrockFunction.Gradient(Point);
         }
 
         protected override void SetHessian()
         {
-            this.HessianRaw = RosenbrockFunction.Hessian(this.Point);
+            HessianRaw = RosenbrockFunction.Hessian(Point);
         }
 
-        public override IEvaluation CreateNew()
+        public override IObjectiveFunction CreateNew()
         {
-            return new RosenbrockEvaluation();
+            return new RosenbrockObjectiveFunction();
         }
     }
 
@@ -42,10 +38,10 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
         [Test]
         public void FindMinimum_Rosenbrock_Easy()
         {
-            var obj = new RosenbrockEvaluation();
+            var obj = new RosenbrockObjectiveFunction();
 
             var solver = new NewtonMinimizer(1e-5, 1000);
-            var result = solver.FindMinimum(obj, new MathNet.Numerics.LinearAlgebra.Double.DenseVector(new double[] { 1.2, 1.2 }));
+            var result = solver.FindMinimum(obj, new DenseVector(new[] { 1.2, 1.2 }));
 
             Assert.That(Math.Abs(result.MinimizingPoint[0] - 1.0), Is.LessThan(1e-3));
             Assert.That(Math.Abs(result.MinimizingPoint[1] - 1.0), Is.LessThan(1e-3));
@@ -54,9 +50,9 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
         [Test]
         public void FindMinimum_Rosenbrock_Hard()
         {
-            var obj = new RosenbrockEvaluation();
+            var obj = new RosenbrockObjectiveFunction();
             var solver = new NewtonMinimizer(1e-5, 1000);
-            var result = solver.FindMinimum(obj, new MathNet.Numerics.LinearAlgebra.Double.DenseVector(new double[] { -1.2, 1.0 }));
+            var result = solver.FindMinimum(obj, new DenseVector(new[] { -1.2, 1.0 }));
 
             Assert.That(Math.Abs(result.MinimizingPoint[0] - 1.0), Is.LessThan(1e-3));
             Assert.That(Math.Abs(result.MinimizingPoint[1] - 1.0), Is.LessThan(1e-3));
@@ -65,9 +61,9 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
         [Test]
         public void FindMinimum_Rosenbrock_Overton()
         {
-            var obj = new RosenbrockEvaluation();
+            var obj = new RosenbrockObjectiveFunction();
             var solver = new NewtonMinimizer(1e-5, 1000);
-            var result = solver.FindMinimum(obj, new MathNet.Numerics.LinearAlgebra.Double.DenseVector(new double[] { -0.9, -0.5 }));
+            var result = solver.FindMinimum(obj, new DenseVector(new[] { -0.9, -0.5 }));
 
             Assert.That(Math.Abs(result.MinimizingPoint[0] - 1.0), Is.LessThan(1e-3));
             Assert.That(Math.Abs(result.MinimizingPoint[1] - 1.0), Is.LessThan(1e-3));
@@ -76,9 +72,9 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
         [Test]
         public void FindMinimum_Linesearch_Rosenbrock_Easy()
         {
-            var obj = new RosenbrockEvaluation();
+            var obj = new RosenbrockObjectiveFunction();
             var solver = new NewtonMinimizer(1e-5, 1000, true);
-            var result = solver.FindMinimum(obj, new MathNet.Numerics.LinearAlgebra.Double.DenseVector(new double[] { 1.2, 1.2 }));
+            var result = solver.FindMinimum(obj, new DenseVector(new[] { 1.2, 1.2 }));
 
             Assert.That(Math.Abs(result.MinimizingPoint[0] - 1.0), Is.LessThan(1e-3));
             Assert.That(Math.Abs(result.MinimizingPoint[1] - 1.0), Is.LessThan(1e-3));
@@ -87,9 +83,9 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
         [Test]
         public void FindMinimum_Linesearch_Rosenbrock_Hard()
         {
-            var obj = new RosenbrockEvaluation();
+            var obj = new RosenbrockObjectiveFunction();
             var solver = new NewtonMinimizer(1e-5, 1000, true);
-            var result = solver.FindMinimum(obj, new MathNet.Numerics.LinearAlgebra.Double.DenseVector(new double[] { -1.2, 1.0 }));
+            var result = solver.FindMinimum(obj, new DenseVector(new[] { -1.2, 1.0 }));
 
             Assert.That(Math.Abs(result.MinimizingPoint[0] - 1.0), Is.LessThan(1e-3));
             Assert.That(Math.Abs(result.MinimizingPoint[1] - 1.0), Is.LessThan(1e-3));
@@ -98,9 +94,9 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
         [Test]
         public void FindMinimum_Linesearch_Rosenbrock_Overton()
         {
-            var obj = new RosenbrockEvaluation();
+            var obj = new RosenbrockObjectiveFunction();
             var solver = new NewtonMinimizer(1e-5, 1000, true);
-            var result = solver.FindMinimum(obj, new MathNet.Numerics.LinearAlgebra.Double.DenseVector(new double[] { -0.9, -0.5 }));
+            var result = solver.FindMinimum(obj, new DenseVector(new[] { -0.9, -0.5 }));
 
             Assert.That(Math.Abs(result.MinimizingPoint[0] - 1.0), Is.LessThan(1e-3));
             Assert.That(Math.Abs(result.MinimizingPoint[1] - 1.0), Is.LessThan(1e-3));