Optimization: first pass code style fix

src/Numerics/Optimization/BaseEvaluation.cs

@@ -1,9 +1,4 @@
-using System;
+using MathNet.Numerics.LinearAlgebra;
-using System.Collections.Generic;
-using System.Linq;
-using System.Text;
-
-using MathNet.Numerics.LinearAlgebra;
 
 namespace MathNet.Numerics.Optimization
 {
@@ -19,23 +14,23 @@ namespace MathNet.Numerics.Optimization
         public bool GradientSupported { get; private set; }
         public bool HessianSupported { get; private set; }
 
-        protected BaseEvaluation(bool gradient_supported, bool hessian_supported)
+        protected BaseEvaluation(bool gradientSupported, bool hessianSupported)
         {
             Status = EvaluationStatus.None;
-            this.GradientSupported = gradient_supported;
+            GradientSupported = gradientSupported;
-            this.HessianSupported = hessian_supported;
+            HessianSupported = hessianSupported;
         }
 
         public Vector<double> Point
         {
             get
             {
-                return this.PointRaw;
+                return PointRaw;
             }
             set
             {
-                this.PointRaw = value;
+                PointRaw = value;
-                this.Status = EvaluationStatus.None;
+                Status = EvaluationStatus.None;
             }
         }
 
@@ -45,7 +40,7 @@ namespace MathNet.Numerics.Optimization
             {
                 if (!Status.HasFlag(EvaluationStatus.Value))
                 {
-                    setValue();
+                    SetValue();
                     Status |= EvaluationStatus.Value;
                 }
                 return ValueRaw;
@@ -57,7 +52,7 @@ namespace MathNet.Numerics.Optimization
             {
                 if (!Status.HasFlag(EvaluationStatus.Gradient))
                 {
-                    setGradient();
+                    SetGradient();
                     Status |= EvaluationStatus.Gradient;
                 }
                 return GradientRaw;
@@ -69,16 +64,16 @@ namespace MathNet.Numerics.Optimization
             {
                 if (!Status.HasFlag(EvaluationStatus.Hessian))
                 {
-                    setHessian();
+                    SetHessian();
                     Status |= EvaluationStatus.Hessian;
                 }
                 return HessianRaw;
             }
         }
 
-        protected abstract void setValue();
+        protected abstract void SetValue();
-        protected abstract void setGradient();
+        protected abstract void SetGradient();
-        protected abstract void setHessian();
+        protected abstract void SetHessian();
         public abstract IEvaluation CreateNew();
     }
 }

src/Numerics/Optimization/Exceptions.cs

@@ -1,7 +1,4 @@
 using System;
-using System.Collections.Generic;
-using System.Linq;
-using System.Text;
 
 namespace MathNet.Numerics.Optimization
 {

src/Numerics/Optimization/IEvaluation.cs

@@ -1,8 +1,5 @@
-using MathNet.Numerics.LinearAlgebra;
+using System;
-using System;
+using MathNet.Numerics.LinearAlgebra;
-using System.Collections.Generic;
-using System.Linq;
-using System.Text;
 
 namespace MathNet.Numerics.Optimization
 {

src/Numerics/Optimization/IUnconstrainedMinimizer.cs

@@ -1,14 +1,9 @@
-using System;
+using MathNet.Numerics.LinearAlgebra;
-using System.Collections.Generic;
-using System.Linq;
-using System.Text;
-using MathNet.Numerics.LinearAlgebra;
 
 namespace MathNet.Numerics.Optimization
 {
     public interface IUnconstrainedMinimizer
     {
-        MinimizationOutput FindMinimum(IEvaluation objective, Vector<double> initial_guess);
+        MinimizationOutput FindMinimum(IEvaluation objective, Vector<double> initialGuess);
     }
-
 }

src/Numerics/Optimization/Implementation/LineSearchOutput.cs

@@ -1,18 +1,13 @@
-using System;
+namespace MathNet.Numerics.Optimization.Implementation
-using System.Collections.Generic;
-using System.Linq;
-using System.Text;
-
-namespace MathNet.Numerics.Optimization.Implementation
 {
     public class LineSearchOutput : MinimizationOutput
     {
         public double FinalStep { get; private set; }
 
-        public LineSearchOutput(IEvaluation function_info, int iterations, double final_step, ExitCondition reason_for_exit)
+        public LineSearchOutput(IEvaluation functionInfo, int iterations, double finalStep, ExitCondition reasonForExit)
-            : base(function_info, iterations, reason_for_exit)
+            : base(functionInfo, iterations, reasonForExit)
         {
-            this.FinalStep = final_step;
+            FinalStep = finalStep;
         }
     }
 }

src/Numerics/Optimization/Implementation/NullEvaluation.cs

@@ -1,30 +1,26 @@
 using System;
-using System.Collections.Generic;
-using System.Linq;
-using System.Text;
-
 using MathNet.Numerics.LinearAlgebra;
 
 namespace MathNet.Numerics.Optimization.Implementation
 {
     public class NullEvaluation : BaseEvaluation
     {
-        public NullEvaluation(Vector<double> point) 
+        public NullEvaluation(Vector<double> point)
             : base(false, false)
         {
-            this.Point = point;
+            Point = point;
         }
-        protected override void setValue()
+        protected override void SetValue()
         {
             throw new NotImplementedException();
         }
 
-        protected override void setGradient()
+        protected override void SetGradient()
         {
             throw new NotImplementedException();
         }
 
-        protected override void setHessian()
+        protected override void SetHessian()
         {
             throw new NotImplementedException();
         }

src/Numerics/Optimization/Implementation/ObjectiveChecker.cs

@@ -1,34 +1,31 @@
 using System;
-using System.Collections.Generic;
-using System.Linq;
-using System.Text;
 using MathNet.Numerics.LinearAlgebra;
 
 namespace MathNet.Numerics.Optimization.Implementation
 {
     public class CheckedEvaluation : IEvaluation
     {
+        private bool _valueChecked;
+        private bool _gradientChecked;
+        private bool _hessianChecked;
+
         public IEvaluation InnerEvaluation { get; private set; }
-        private bool ValueChecked;
-        private bool GradientChecked;
-        private bool HessianChecked;
         public Action<IEvaluation> ValueChecker { get; private set; }
         public Action<IEvaluation> GradientChecker { get; private set; }
         public Action<IEvaluation> HessianChecker { get; private set; }
 
-
+        public CheckedEvaluation(IEvaluation objective, Action<IEvaluation> valueChecker, Action<IEvaluation> gradientChecker, Action<IEvaluation> hessianChecker)
-        public CheckedEvaluation(IEvaluation objective, Action<IEvaluation> value_checker, Action<IEvaluation> gradient_checker, Action<IEvaluation> hessian_checker)
         {
-            this.InnerEvaluation = objective;
+            InnerEvaluation = objective;
-            this.ValueChecker = value_checker;
+            ValueChecker = valueChecker;
-            this.GradientChecker = gradient_checker;
+            GradientChecker = gradientChecker;
-            this.HessianChecker = hessian_checker;
+            HessianChecker = hessianChecker;
         }
 
         public Vector<double> Point
         {
-            get { return this.InnerEvaluation.Point; }
+            get { return InnerEvaluation.Point; }
-            set { this.InnerEvaluation.Point = value; }
+            set { InnerEvaluation.Point = value; }
         }
 
         public double Value
@@ -36,21 +33,21 @@ namespace MathNet.Numerics.Optimization.Implementation
             get
             {
 
-                if (!this.ValueChecked)
+                if (!_valueChecked)
                 {
                     double tmp;
                     try
                     {
-                        tmp = this.InnerEvaluation.Value;
+                        tmp = InnerEvaluation.Value;
                     }
                     catch (Exception e)
                     {
-                        throw new EvaluationException("Objective function evaluation failed.", this.InnerEvaluation, e);
+                        throw new EvaluationException("Objective function evaluation failed.", InnerEvaluation, e);
                     }
-                    this.ValueChecker(this.InnerEvaluation);
+                    ValueChecker(InnerEvaluation);
-                    this.ValueChecked = true;
+                    _valueChecked = true;
                 }
-                return this.InnerEvaluation.Value;
+                return InnerEvaluation.Value;
             }
         }
 
@@ -59,21 +56,21 @@ namespace MathNet.Numerics.Optimization.Implementation
             get
             {
 
-                if (!this.GradientChecked)
+                if (!_gradientChecked)
                 {
                     Vector<double> tmp;
                     try
                     {
-                        tmp = this.InnerEvaluation.Gradient;
+                        tmp = InnerEvaluation.Gradient;
                     }
                     catch (Exception e)
                     {
-                        throw new EvaluationException("Objective gradient evaluation failed.", this.InnerEvaluation, e);
+                        throw new EvaluationException("Objective gradient evaluation failed.", InnerEvaluation, e);
                     }
-                    this.GradientChecker(this.InnerEvaluation);
+                    GradientChecker(InnerEvaluation);
-                    this.GradientChecked = true;
+                    _gradientChecked = true;
                 }
-                return this.InnerEvaluation.Gradient;
+                return InnerEvaluation.Gradient;
             }
         }
 
@@ -82,37 +79,37 @@ namespace MathNet.Numerics.Optimization.Implementation
             get
             {
 
-                if (!this.HessianChecked)
+                if (!_hessianChecked)
                 {
                     Matrix<double> tmp;
                     try
                     {
-                        tmp = this.InnerEvaluation.Hessian;
+                        tmp = InnerEvaluation.Hessian;
                     }
                     catch (Exception e)
                     {
-                        throw new EvaluationException("Objective hessian evaluation failed.", this.InnerEvaluation, e);
+                        throw new EvaluationException("Objective hessian evaluation failed.", InnerEvaluation, e);
                     }
-                    this.HessianChecker(InnerEvaluation);
+                    HessianChecker(InnerEvaluation);
-                    this.HessianChecked = true;
+                    _hessianChecked = true;
                 }
-                return this.InnerEvaluation.Hessian;
+                return InnerEvaluation.Hessian;
             }
         }
 
         public IEvaluation CreateNew()
         {
-            return new CheckedEvaluation(this.InnerEvaluation, this.ValueChecker, this.GradientChecker, this.HessianChecker);
+            return new CheckedEvaluation(InnerEvaluation, ValueChecker, GradientChecker, HessianChecker);
         }
 
         public bool GradientSupported
         {
-            get { return this.InnerEvaluation.GradientSupported; }
+            get { return InnerEvaluation.GradientSupported; }
         }
 
         public bool HessianSupported
         {
-            get { return this.InnerEvaluation.HessianSupported; }
+            get { return InnerEvaluation.HessianSupported; }
         }
     }
 }

src/Numerics/Optimization/Implementation/WeakWolfeLineSearch.cs

@@ -1,117 +1,122 @@
 using System;
-using System.Collections.Generic;
-using System.Linq;
-using System.Text;
-
 using MathNet.Numerics.LinearAlgebra;
 
 namespace MathNet.Numerics.Optimization.Implementation
 {
     public class WeakWolfeLineSearch
     {
-        public double C1 { get; set; }
+        readonly double _c1;
-        public double C2 { get; set; }
+        readonly double _c2;
-        public double ParameterTolerance { get; set; }
+        readonly double _parameterTolerance;
-        public int MaximumIterations { get; set; }
+        readonly int _maximumIterations;
 
-        public WeakWolfeLineSearch(double c1, double c2, double parameter_tolerance, int max_iterations = 10)
+        public WeakWolfeLineSearch(double c1, double c2, double parameterTolerance, int maxIterations = 10)
         {
-            this.C1 = c1;
+            _c1 = c1;
-            this.C2 = c2;
+            _c2 = c2;
-            this.ParameterTolerance = parameter_tolerance;
+            _parameterTolerance = parameterTolerance;
-            this.MaximumIterations = max_iterations;
+            _maximumIterations = maxIterations;
         }
 
         // Implemented following http://www.math.washington.edu/~burke/crs/408/lectures/L9-weak-Wolfe.pdf
-        public LineSearchOutput FindConformingStep(IEvaluation objective, IEvaluation starting_point, Vector<double> search_direction, double initial_step)
+        public LineSearchOutput FindConformingStep(IEvaluation objective, IEvaluation startingPoint, Vector<double> searchDirection, double initialStep)
         {
-
             if (!(objective is CheckedEvaluation))
-                objective = new CheckedEvaluation(objective, this.ValidateValue, this.ValidateGradient, null);
+            {
+                objective = new CheckedEvaluation(objective, ValidateValue, ValidateGradient, null);
+            }
 
-            double lower_bound = 0.0;
+            double lowerBound = 0.0;
-            double upper_bound = Double.PositiveInfinity;
+            double upperBound = Double.PositiveInfinity;
-            double step = initial_step;
+            double step = initialStep;
 
-            double initial_value = starting_point.Value;
+            double initialValue = startingPoint.Value;
-            Vector<double> initial_gradient = starting_point.Gradient;
+            Vector<double> initialGradient = startingPoint.Gradient;
 
-            double initial_dd = search_direction * initial_gradient;
+            double initialDd = searchDirection * initialGradient;
 
             int ii;
-            IEvaluation candidate_eval = objective.CreateNew();
+            IEvaluation candidateEval = objective.CreateNew();
-            MinimizationOutput.ExitCondition reason_for_exit = MinimizationOutput.ExitCondition.None;
+            MinimizationOutput.ExitCondition reasonForExit = MinimizationOutput.ExitCondition.None;
-            for (ii = 0; ii < this.MaximumIterations; ++ii)
+            for (ii = 0; ii < _maximumIterations; ++ii)
             {
-                candidate_eval.Point = starting_point.Point + search_direction * step;
+                candidateEval.Point = startingPoint.Point + searchDirection * step;
 
-                double step_dd = search_direction * candidate_eval.Gradient;
+                double stepDd = searchDirection * candidateEval.Gradient;
 
-                if (candidate_eval.Value > initial_value + this.C1 * step * initial_dd)
+                if (candidateEval.Value > initialValue + _c1 * step * initialDd)
                 {
-                    upper_bound = step;
+                    upperBound = step;
-                    step = 0.5 * (lower_bound + upper_bound);
+                    step = 0.5 * (lowerBound + upperBound);
                 }
-                else if (step_dd < this.C2 * initial_dd)
+                else if (stepDd < _c2 * initialDd)
                 {
-                    lower_bound = step;
+                    lowerBound = step;
-                    step = Double.IsPositiveInfinity(upper_bound) ? 2 * lower_bound : 0.5 * (lower_bound + upper_bound);
+                    step = Double.IsPositiveInfinity(upperBound) ? 2 * lowerBound : 0.5 * (lowerBound + upperBound);
                 }
                 else
                 {
-                    reason_for_exit = MinimizationOutput.ExitCondition.WeakWolfeCriteria;
+                    reasonForExit = MinimizationOutput.ExitCondition.WeakWolfeCriteria;
                     break;
                 }
 
-                if (!Double.IsInfinity(upper_bound))
+                if (!Double.IsInfinity(upperBound))
                 {
-                    double max_rel_change = 0.0;
+                    double maxRelChange = 0.0;
-                    for (int jj = 0; jj < candidate_eval.Point.Count; ++jj)
+                    for (int jj = 0; jj < candidateEval.Point.Count; ++jj)
                     {
-                        double tmp = Math.Abs(search_direction[jj] * (upper_bound - lower_bound)) / Math.Max(Math.Abs(candidate_eval.Point[jj]), 1.0);
+                        double tmp = Math.Abs(searchDirection[jj] * (upperBound - lowerBound)) / Math.Max(Math.Abs(candidateEval.Point[jj]), 1.0);
-                        max_rel_change = Math.Max(max_rel_change, tmp);
+                        maxRelChange = Math.Max(maxRelChange, tmp);
                     }
-                    if (max_rel_change < this.ParameterTolerance)
+                    if (maxRelChange < _parameterTolerance)
                     {
-                        reason_for_exit = MinimizationOutput.ExitCondition.LackOfProgress;
+                        reasonForExit = MinimizationOutput.ExitCondition.LackOfProgress;
                         break;
                     }
                 }
             }
 
-            if (ii == this.MaximumIterations && Double.IsPositiveInfinity(upper_bound))
+            if (ii == _maximumIterations && Double.IsPositiveInfinity(upperBound))
-                throw new MaximumIterationsException(String.Format("Maximum iterations ({0}) reached. Function appears to be unbounded in search direction.", this.MaximumIterations));
+            {
-            else if (ii == this.MaximumIterations)
+                throw new MaximumIterationsException(String.Format("Maximum iterations ({0}) reached. Function appears to be unbounded in search direction.", _maximumIterations));
-                throw new MaximumIterationsException(String.Format("Maximum iterations ({0}) reached.", this.MaximumIterations));
+            }
-            else
+
-                return new LineSearchOutput(candidate_eval, ii, step, reason_for_exit);
+            if (ii == _maximumIterations)
+            {
+                throw new MaximumIterationsException(String.Format("Maximum iterations ({0}) reached.", _maximumIterations));
+            }
+
+            return new LineSearchOutput(candidateEval, ii, step, reasonForExit);
         }
 
-        private bool Conforms(IEvaluation starting_point, Vector<double> search_direction, double step, IEvaluation ending_point)
+        bool Conforms(IEvaluation startingPoint, Vector<double> searchDirection, double step, IEvaluation endingPoint)
         {
+            bool sufficientDecrease = endingPoint.Value <= startingPoint.Value + _c1 * step * (startingPoint.Gradient * searchDirection);
+            bool notTooSteep = endingPoint.Gradient * searchDirection >= _c2 * startingPoint.Gradient * searchDirection;
 
-            bool sufficient_decrease = ending_point.Value <= starting_point.Value + this.C1 * step * (starting_point.Gradient * search_direction);
+            return step > 0 && sufficientDecrease && notTooSteep;
-            bool not_too_steep = ending_point.Gradient * search_direction >= this.C2 * starting_point.Gradient * search_direction;
-
-            return step > 0 && sufficient_decrease && not_too_steep;
         }
 
-        private void ValidateValue(IEvaluation eval)
+        void ValidateValue(IEvaluation eval)
         {
-            if (!this.IsFinite(eval.Value))
+            if (!IsFinite(eval.Value))
+            {
                 throw new EvaluationException(String.Format("Non-finite value returned by objective function: {0}", eval.Value), eval);
+            }
         }
 
-        private void ValidateGradient(IEvaluation eval)
+        void ValidateGradient(IEvaluation eval)
         {
             foreach (double x in eval.Gradient)
-                if (!this.IsFinite(x))
+            {
+                if (!IsFinite(x))
                 {
                     throw new EvaluationException(String.Format("Non-finite value returned by gradient: {0}", x), eval);
                 }
+            }
         }
 
-        private bool IsFinite(double x)
+        bool IsFinite(double x)
         {
             return !(Double.IsNaN(x) || Double.IsInfinity(x));
         }

src/Numerics/Optimization/MinimizationOutput.cs

@@ -1,8 +1,4 @@
-using System;
+using MathNet.Numerics.LinearAlgebra;
-using System.Collections.Generic;
-using System.Linq;
-using System.Text;
-using MathNet.Numerics.LinearAlgebra;
 
 namespace MathNet.Numerics.Optimization
 {
@@ -15,11 +11,11 @@ namespace MathNet.Numerics.Optimization
         public int Iterations { get; private set; }
         public ExitCondition ReasonForExit { get; private set; }
 
-        public MinimizationOutput(IEvaluation function_info, int iterations, ExitCondition reason_for_exit)
+        public MinimizationOutput(IEvaluation functionInfo, int iterations, ExitCondition reasonForExit)
         {
-            this.FunctionInfoAtMinimum = function_info;
+            FunctionInfoAtMinimum = functionInfo;
-            this.Iterations = iterations;
+            Iterations = iterations;
-            this.ReasonForExit = reason_for_exit;
+            ReasonForExit = reasonForExit;
         }
     }
 }

src/Numerics/Optimization/MinimizationWithLineSearchOutput.cs

@@ -1,20 +1,15 @@
-using System;
+namespace MathNet.Numerics.Optimization
-using System.Collections.Generic;
-using System.Linq;
-using System.Text;
-
-namespace MathNet.Numerics.Optimization
 {
     public class MinimizationWithLineSearchOutput : MinimizationOutput
     {
         public int TotalLineSearchIterations { get; private set; }
         public int IterationsWithNonTrivialLineSearch { get; private set; }
 
-        public MinimizationWithLineSearchOutput(IEvaluation function_info, int iterations, ExitCondition reason_for_exit, int total_line_search_iterations, int iterations_with_non_trivial_line_search)
+        public MinimizationWithLineSearchOutput(IEvaluation functionInfo, int iterations, ExitCondition reasonForExit, int totalLineSearchIterations, int iterationsWithNonTrivialLineSearch)
-            : base(function_info, iterations, reason_for_exit)
+            : base(functionInfo, iterations, reasonForExit)
         {
-            this.TotalLineSearchIterations = total_line_search_iterations;
+            TotalLineSearchIterations = totalLineSearchIterations;
-            this.IterationsWithNonTrivialLineSearch = iterations_with_non_trivial_line_search;
+            IterationsWithNonTrivialLineSearch = iterationsWithNonTrivialLineSearch;
         }
     }
 }

src/Numerics/Optimization/NewtonMinimizer.cs

@@ -1,10 +1,7 @@
 using System;
-using System.Collections.Generic;
-using System.Linq;
-using System.Text;
 using MathNet.Numerics.LinearAlgebra;
-using LU = MathNet.Numerics.LinearAlgebra.Factorization.LU<double>;
 using MathNet.Numerics.Optimization.Implementation;
+using LU = MathNet.Numerics.LinearAlgebra.Factorization.LU<double>;
 
 namespace MathNet.Numerics.Optimization
 {
@@ -14,14 +11,14 @@ namespace MathNet.Numerics.Optimization
         public int MaximumIterations { get; set; }
         public bool UseLineSearch { get; set; }
 
-        public NewtonMinimizer(double gradient_tolerance, int maximum_iterations, bool use_line_search = false)
+        public NewtonMinimizer(double gradientTolerance, int maximumIterations, bool useLineSearch = false)
         {
-            this.GradientTolerance = gradient_tolerance;
+            GradientTolerance = gradientTolerance;
-            this.MaximumIterations = maximum_iterations;
+            MaximumIterations = maximumIterations;
-            this.UseLineSearch = use_line_search;
+            UseLineSearch = useLineSearch;
         }
 
-        public MinimizationOutput FindMinimum(IEvaluation objective, Vector<double> initial_guess)
+        public MinimizationOutput FindMinimum(IEvaluation objective, Vector<double> initialGuess)
         {
             if (!objective.GradientSupported)
                 throw new IncompatibleObjectiveException("Gradient not supported in objective function, but required for Newton minimization.");
@@ -30,74 +27,69 @@ namespace MathNet.Numerics.Optimization
                 throw new IncompatibleObjectiveException("Hessian not supported in objective function, but required for Newton minimization.");
 
             if (!(objective is CheckedEvaluation))
-                objective = new CheckedEvaluation(objective, this.ValidateObjective, this.ValidateGradient, this.ValidateHessian);
+                objective = new CheckedEvaluation(objective, ValidateObjective, ValidateGradient, ValidateHessian);
 
-            IEvaluation initial_eval = objective.CreateNew();
+            IEvaluation initialEval = objective.CreateNew();
-            initial_eval.Point = initial_guess;
+            initialEval.Point = initialGuess;
 
             // Check that we're not already done
-            if (this.ExitCriteriaSatisfied(initial_guess, initial_eval.Gradient))
+            if (ExitCriteriaSatisfied(initialGuess, initialEval.Gradient))
-                return new MinimizationOutput(initial_eval, 0, MinimizationOutput.ExitCondition.AbsoluteGradient);
+                return new MinimizationOutput(initialEval, 0, MinimizationOutput.ExitCondition.AbsoluteGradient);
 
-            // Set up line search algorithm            
+            // Set up line search algorithm
-            var line_searcher = new WeakWolfeLineSearch(1e-4, 0.9, 1e-4, max_iterations: 1000);
+            var lineSearcher = new WeakWolfeLineSearch(1e-4, 0.9, 1e-4, maxIterations: 1000);
 
             // Declare state variables
-            IEvaluation candidate_point = initial_eval;
+            IEvaluation candidatePoint = initialEval;
-            Vector<double> search_direction;
-            LineSearchOutput result;
 
             // Subsequent steps
             int iterations = 0;
-            int total_line_search_steps = 0;
+            int totalLineSearchSteps = 0;
-            int iterations_with_nontrivial_line_search = 0;
+            int iterationsWithNontrivialLineSearch = 0;
-            int steepest_descent_resets = 0;
+            bool tmpLineSearch = false;
-            bool tmp_line_search = false;
+            while (!ExitCriteriaSatisfied(candidatePoint.Point, candidatePoint.Gradient) && iterations < MaximumIterations)
-            while (!this.ExitCriteriaSatisfied(candidate_point.Point, candidate_point.Gradient) && iterations < this.MaximumIterations)
             {
-                
+                var searchDirection = candidatePoint.Hessian.LU().Solve(-candidatePoint.Gradient);
-                search_direction = candidate_point.Hessian.LU().Solve(-candidate_point.Gradient);
+                if (searchDirection * candidatePoint.Gradient >= 0)
-
-                if (search_direction * candidate_point.Gradient >= 0)
                 {
-                    search_direction = -candidate_point.Gradient;
+                    searchDirection = -candidatePoint.Gradient;
-                    steepest_descent_resets += 1;
+                    tmpLineSearch = true;
-                    tmp_line_search = true;
                 }
 
-                if (this.UseLineSearch || tmp_line_search)
+                if (UseLineSearch || tmpLineSearch)
                 {
+                    LineSearchOutput result;
                     try
                     {
-                        result = line_searcher.FindConformingStep(objective, candidate_point, search_direction, 1.0);
+                        result = lineSearcher.FindConformingStep(objective, candidatePoint, searchDirection, 1.0);
                     }
                     catch (Exception e)
                     {
                         throw new InnerOptimizationException("Line search failed.", e);
                     }
-                    iterations_with_nontrivial_line_search += result.Iterations > 0 ? 1 : 0;
+                    iterationsWithNontrivialLineSearch += result.Iterations > 0 ? 1 : 0;
-                    total_line_search_steps += result.Iterations;
+                    totalLineSearchSteps += result.Iterations;
-                    candidate_point = result.FunctionInfoAtMinimum;
+                    candidatePoint = result.FunctionInfoAtMinimum;
                 }
                 else
                 {
-                    candidate_point.Point = candidate_point.Point + search_direction;
+                    candidatePoint.Point = candidatePoint.Point + searchDirection;
                 }
 
-                tmp_line_search = false;
+                tmpLineSearch = false;
 
                 iterations += 1;
             }
 
-            if (iterations == this.MaximumIterations)
+            if (iterations == MaximumIterations)
-                throw new MaximumIterationsException(String.Format("Maximum iterations ({0}) reached.", this.MaximumIterations));
+                throw new MaximumIterationsException(String.Format("Maximum iterations ({0}) reached.", MaximumIterations));
 
-            return new MinimizationWithLineSearchOutput(candidate_point, iterations, MinimizationOutput.ExitCondition.AbsoluteGradient, total_line_search_steps, iterations_with_nontrivial_line_search);
+            return new MinimizationWithLineSearchOutput(candidatePoint, iterations, MinimizationOutput.ExitCondition.AbsoluteGradient, totalLineSearchSteps, iterationsWithNontrivialLineSearch);
         }
 
-        private bool ExitCriteriaSatisfied(Vector<double> candidate_point, Vector<double> gradient)
+        private bool ExitCriteriaSatisfied(Vector<double> candidatePoint, Vector<double> gradient)
         {
-            return gradient.Norm(2.0) < this.GradientTolerance;
+            return gradient.Norm(2.0) < GradientTolerance;
         }
 
         private void ValidateGradient(IEvaluation eval)

src/UnitTests/OptimizationTests/TestNewtonMinimizer.cs

@@ -14,17 +14,17 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
         public RosenbrockEvaluation()
             : base(true, true) { }
 
-        protected override void setValue()
+        protected override void SetValue()
         {
             this.ValueRaw = RosenbrockFunction.Value(this.Point);
         }
 
-        protected override void setGradient()
+        protected override void SetGradient()
         {
             this.GradientRaw = RosenbrockFunction.Gradient(this.Point);
         }
 
-        protected override void setHessian()
+        protected override void SetHessian()
         {
             this.HessianRaw = RosenbrockFunction.Hessian(this.Point);
         }