Remove static properties in LevenbergMarquardtMinimizer. See #672

src/Numerics/Optimization/LevenbergMarquardtMinimizer.cs

@@ -10,7 +10,7 @@ namespace MathNet.Numerics.Optimization
         /// <summary>
         /// The scale factor for initial mu
         /// </summary>
-        public static double InitialMu { get; set; }
+        public double InitialMu { get; set; } = 1E-3;
 
         public LevenbergMarquardtMinimizer(double initialMu = 1E-3, double gradientTolerance = 1E-15, double stepTolerance = 1E-15, double functionTolerance = 1E-15, int maximumIterations = -1)
             : base(gradientTolerance, stepTolerance, functionTolerance, maximumIterations)
@@ -51,7 +51,7 @@ namespace MathNet.Numerics.Optimization
         /// <param name="functionTolerance">The stopping threshold for L2 norm of the residuals.</param>
         /// <param name="maximumIterations">The max iterations.</param>
         /// <returns>The result of the Levenberg-Marquardt minimization</returns>
-        public static NonlinearMinimizationResult Minimum(IObjectiveModel objective, Vector<double> initialGuess,
+        public NonlinearMinimizationResult Minimum(IObjectiveModel objective, Vector<double> initialGuess,
             Vector<double> lowerBound = null, Vector<double> upperBound = null, Vector<double> scales = null, List<bool> isFixed = null,
             double initialMu = 1E-3, double gradientTolerance = 1E-15, double stepTolerance = 1E-15, double functionTolerance = 1E-15, int maximumIterations = -1)
         {

src/Numerics/Optimization/NonlinearMinimizerBase.cs

@@ -9,39 +9,39 @@ namespace MathNet.Numerics.Optimization
         /// <summary>
         /// The stopping threshold for the function value or L2 norm of the residuals.
         /// </summary>
-        public static double FunctionTolerance { get; set; }
+        public double FunctionTolerance { get; set; }
 
         /// <summary>
         /// The stopping threshold for L2 norm of the change of the parameters.
         /// </summary>
-        public static double StepTolerance { get; set; }
+        public double StepTolerance { get; set; }
 
         /// <summary>
         /// The stopping threshold for infinity norm of the gradient.
         /// </summary>
-        public static double GradientTolerance { get; set; }
+        public double GradientTolerance { get; set; }
 
         /// <summary>
         /// The maximum number of iterations.
         /// </summary>
-        public static int MaximumIterations { get; set; }
+        public int MaximumIterations { get; set; }
 
         /// <summary>
         /// The lower bound of the parameters.
         /// </summary>
-        public static Vector<double> LowerBound { get; private set; }
+        public Vector<double> LowerBound { get; private set; }
 
         /// <summary>
         /// The upper bound of the parameters.
         /// </summary>
-        public static Vector<double> UpperBound { get; private set; }
+        public Vector<double> UpperBound { get; private set; }
 
         /// <summary>
         /// The scale factors for the parameters.
         /// </summary>
-        public static Vector<double> Scales { get; private set; }
+        public Vector<double> Scales { get; private set; }
 
-        private static bool IsBounded => LowerBound != null || UpperBound != null || Scales != null;
+        private bool IsBounded => LowerBound != null || UpperBound != null || Scales != null;
 
         protected NonlinearMinimizerBase(double gradientTolerance = 1E-18, double stepTolerance = 1E-18, double functionTolerance = 1E-18, int maximumIterations = -1)
         {
@@ -51,7 +51,7 @@ namespace MathNet.Numerics.Optimization
             MaximumIterations = maximumIterations;
         }
 
-        protected static void ValidateBounds(Vector<double> parameters, Vector<double> lowerBound = null, Vector<double> upperBound = null, Vector<double> scales = null)
+        protected void ValidateBounds(Vector<double> parameters, Vector<double> lowerBound = null, Vector<double> upperBound = null, Vector<double> scales = null)
         {
             if (parameters == null)
             {
@@ -93,14 +93,14 @@ namespace MathNet.Numerics.Optimization
             Scales = scales;
         }
 
-        protected static double EvaluateFunction(IObjectiveModel objective, Vector<double> Pint)
+        protected double EvaluateFunction(IObjectiveModel objective, Vector<double> Pint)
         {
             var Pext = ProjectToExternalParameters(Pint);
             objective.EvaluateAt(Pext);
             return objective.Value;
         }
 
-        protected static Tuple<Vector<double>, Matrix<double>> EvaluateJacobian(IObjectiveModel objective, Vector<double> Pint)
+        protected Tuple<Vector<double>, Matrix<double>> EvaluateJacobian(IObjectiveModel objective, Vector<double> Pint)
         {
             var gradient = objective.Gradient;
             var hessian = objective.Hessian;
@@ -161,7 +161,7 @@ namespace MathNet.Numerics.Optimization
         // Except when it is initial guess, the parameters argument is always internal parameter.
         // So, first map the parameters argument to the external parameters in order to calculate function values.
 
-        protected static Vector<double> ProjectToInternalParameters(Vector<double> Pext)
+        protected Vector<double> ProjectToInternalParameters(Vector<double> Pext)
         {
             var Pint = Pext.Clone();
 
@@ -209,7 +209,7 @@ namespace MathNet.Numerics.Optimization
             return Pint;
         }
 
-        protected static Vector<double> ProjectToExternalParameters(Vector<double> Pint)
+        protected Vector<double> ProjectToExternalParameters(Vector<double> Pint)
         {
             var Pext = Pint.Clone();
 
@@ -257,7 +257,7 @@ namespace MathNet.Numerics.Optimization
             return Pext;
         }
 
-        protected static Vector<double> ScaleFactorsOfJacobian(Vector<double> Pint)
+        protected Vector<double> ScaleFactorsOfJacobian(Vector<double> Pint)
         {
             var scale = Vector<double>.Build.Dense(Pint.Count, 1.0);
 

src/Numerics/Optimization/TrustRegion/TrustRegionMinimizerBase.cs

@@ -10,12 +10,12 @@ namespace MathNet.Numerics.Optimization.TrustRegion
         /// <summary>
         /// The trust region subproblem.
         /// </summary>
-        public static ITrustRegionSubproblem Subproblem;
+        public ITrustRegionSubproblem Subproblem;
 
         /// <summary>
         /// The stopping threshold for the trust region radius.
         /// </summary>
-        public static double RadiusTolerance { get; set; }
+        public double RadiusTolerance { get; set; }
 
         public TrustRegionMinimizerBase(ITrustRegionSubproblem subproblem,
             double gradientTolerance = 1E-8, double stepTolerance = 1E-8, double functionTolerance = 1E-8, double radiusTolerance = 1E-8, int maximumIterations = -1)
@@ -59,7 +59,7 @@ namespace MathNet.Numerics.Optimization.TrustRegion
         /// <param name="radiusTolerance">The stopping threshold for trust region radius</param>
         /// <param name="maximumIterations">The max iterations.</param>
         /// <returns></returns>
-        public static NonlinearMinimizationResult Minimum(ITrustRegionSubproblem subproblem, IObjectiveModel objective, Vector<double> initialGuess,
+        public NonlinearMinimizationResult Minimum(ITrustRegionSubproblem subproblem, IObjectiveModel objective, Vector<double> initialGuess,
             Vector<double> lowerBound = null, Vector<double> upperBound = null, Vector<double> scales = null, List<bool> isFixed = null,
             double gradientTolerance = 1E-8, double stepTolerance = 1E-8, double functionTolerance = 1E-8, double radiusTolerance = 1E-18, int maximumIterations = -1)
         {