Optimization: restore objective function CreateNew (but keep Fork as well)

src/Numerics/Optimization/BaseObjectiveFunction.cs

@@ -87,7 +87,18 @@ namespace MathNet.Numerics.Optimization
             }
         }
 
-        public abstract IObjectiveFunction Fork();
+        public abstract IObjectiveFunction CreateNew();
+
+        public virtual IObjectiveFunction Fork()
+        {
+            BaseObjectiveFunction fork = (BaseObjectiveFunction)CreateNew();
+            fork.PointRaw = PointRaw;
+            fork.ValueRaw = ValueRaw;
+            fork.GradientRaw = GradientRaw;
+            fork.HessianRaw = HessianRaw;
+            fork.Status = Status;
+            return fork;
+        }
 
         protected abstract void SetValue();
         protected abstract void SetGradient();

src/Numerics/Optimization/IObjectiveFunction.cs

@@ -2,8 +2,15 @@
 
 namespace MathNet.Numerics.Optimization
 {
+    /// <summary>
+    /// Objective function with a frozen evaluation that must not be changed from the outside.
+    /// </summary>
     public interface IObjectiveFunctionEvaluation
     {
+        /// <summary>Create a new unevaluated and independent copy of this objective function</summary>
+        IObjectiveFunction CreateNew();
+
+        /// <summary>Create a new independent copy of this objective function, evaluated at the same point.</summary>
         IObjectiveFunction Fork();
 
         Vector<double> Point { get; }
@@ -16,6 +23,9 @@ namespace MathNet.Numerics.Optimization
         Matrix<double> Hessian { get; }
     }
 
+    /// <summary>
+    /// Objective function with a mutable evaluation.
+    /// </summary>
     public interface IObjectiveFunction : IObjectiveFunctionEvaluation
     {
         void EvaluateAt(Vector<double> point);

src/Numerics/Optimization/Implementation/CheckedObjectiveFunction.cs

@@ -103,9 +103,14 @@ namespace MathNet.Numerics.Optimization.Implementation
             }
         }
 
+        public IObjectiveFunction CreateNew()
+        {
+            return new CheckedObjectiveFunction(InnerObjectiveFunction.CreateNew(), ValueChecker, GradientChecker, HessianChecker);
+        }
+
         public IObjectiveFunction Fork()
         {
-            return new CheckedObjectiveFunction(InnerObjectiveFunction, ValueChecker, GradientChecker, HessianChecker);
+            return new CheckedObjectiveFunction(InnerObjectiveFunction.Fork(), ValueChecker, GradientChecker, HessianChecker);
         }
 
         public bool IsGradientSupported

src/Numerics/Optimization/Implementation/WeakWolfeLineSearch.cs

@@ -21,8 +21,6 @@ namespace MathNet.Numerics.Optimization.Implementation
         // Implemented following http://www.math.washington.edu/~burke/crs/408/lectures/L9-weak-Wolfe.pdf
         public LineSearchOutput FindConformingStep(IObjectiveFunctionEvaluation startingPoint, Vector<double> searchDirection, double initialStep)
         {
-            var objective = startingPoint.Fork();
-
             double lowerBound = 0.0;
             double upperBound = Double.PositiveInfinity;
             double step = initialStep;
@@ -33,6 +31,7 @@ namespace MathNet.Numerics.Optimization.Implementation
 
             double initialDd = searchDirection * initialGradient;
 
+            var objective = startingPoint.CreateNew();
             int ii;
             MinimizationOutput.ExitCondition reasonForExit = MinimizationOutput.ExitCondition.None;
             for (ii = 0; ii < _maximumIterations; ++ii)

src/Numerics/Optimization/NewtonMinimizer.cs

@@ -62,7 +62,7 @@ namespace MathNet.Numerics.Optimization
                     LineSearchOutput result;
                     try
                     {
-                        result = lineSearcher.FindConformingStep(objective.Fork(), searchDirection, 1.0);
+                        result = lineSearcher.FindConformingStep(objective, searchDirection, 1.0);
                     }
                     catch (Exception e)
                     {

src/UnitTests/OptimizationTests/TestNewtonMinimizer.cs

@@ -25,7 +25,7 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
             HessianRaw = RosenbrockFunction.Hessian(Point);
         }
 
-        public override IObjectiveFunction Fork()
+        public override IObjectiveFunction CreateNew()
         {
             return new RosenbrockObjectiveFunction();
         }