Very minimally tested ConjugateGradient minimizer is complete.

src/Numerics/Numerics.csproj

@@ -199,6 +199,8 @@
     <Compile Include="Optimization\BisectionRootFinder.cs" />
     <Compile Include="Optimization\ConjugateGradientMinimizer.cs" />
     <Compile Include="Optimization\GoldenSectionMinimizer.cs" />
+    <Compile Include="Optimization\LineSearchingMinimizerOutput.cs" />
+    <Compile Include="Optimization\LineSearchOutput.cs" />
     <Compile Include="Optimization\MinimizationOutput.cs" />
     <Compile Include="Optimization\ObjectiveFunction.cs" />
     <Compile Include="Optimization\OptimizationResult.cs" />

src/Numerics/Optimization/ConjugateGradientMinimizer.cs

@@ -32,7 +32,7 @@ namespace MathNet.Numerics.Optimization
                 return new MinimizationOutput(initial_eval, 0);
             
             // Set up line search algorithm
-            var line_searcher = new WeakWolfeLineSearch(1e-4, 0.1);
+            var line_searcher = new WeakWolfeLineSearch(1e-4, 0.1,1000);
 
             // Declare state variables
             IEvaluation candidate_point;
@@ -41,26 +41,51 @@ namespace MathNet.Numerics.Optimization
             // First step
             steepest_direction = -gradient;
             search_direction = steepest_direction;
-            var result = line_searcher.FindConformingStep(objective, initial_eval, search_direction, 1.0);
+            double initial_step_size = 100 * this.GradientTolerance / (gradient * gradient);
+            var result = line_searcher.FindConformingStep(objective, initial_eval, search_direction, initial_step_size);
             candidate_point = result.FunctionInfoAtMinimum;
             this.ValidateGradient(candidate_point.Gradient, candidate_point.Point);
             
+			double step_size = (candidate_point.Point - initial_guess).Norm(2.0);
             // Subsequent steps
             int iterations = 1;
+            int total_line_search_steps = result.Iterations;
+            int no_line_search_iterations = result.Iterations > 0 ? 0 : 1;
+            int steepest_descent_resets = 0;
             while (!this.ExitCriteriaSatisfied(candidate_point.Point, candidate_point.Gradient) && iterations < this.MaximumIterations)
             {
                 previous_steepest_direction = steepest_direction;
                 steepest_direction = -candidate_point.Gradient;
-                var search_direction_adjuster = steepest_direction * (steepest_direction - previous_steepest_direction) / (previous_steepest_direction * previous_steepest_direction);
-                search_direction = steepest_direction + search_direction_adjuster * previous_steepest_direction;
-                result = line_searcher.FindConformingStep(objective, candidate_point, search_direction, 1.0);
+                var search_direction_adjuster = Math.Max(0,steepest_direction * (steepest_direction - previous_steepest_direction) / (previous_steepest_direction * previous_steepest_direction));
+                
+                //double prev_grad_mag = previous_steepest_direction*previous_steepest_direction;
+                //double grad_overlap = steepest_direction*previous_steepest_direction;
+                //double search_grad_overlap = candidate_point.Gradient*search_direction;
 
-                candidate_point = result.FunctionInfoAtMinimum;                
+                //if (iterations % initial_guess.Count == 0 || (Math.Abs(grad_overlap) >= 0.2 * prev_grad_mag) || (-2 * prev_grad_mag >= search_grad_overlap) || (search_grad_overlap >= -0.2 * prev_grad_mag))
+                //    search_direction = steepest_direction;
+                //else 
+                //    search_direction = steepest_direction + search_direction_adjuster * search_direction;
+
+                search_direction = steepest_direction + search_direction_adjuster * search_direction;
+                if (search_direction * candidate_point.Gradient >= 0)
+                {
+                    search_direction = steepest_direction;
+                    steepest_descent_resets += 1;
+                }
+                
+                result = line_searcher.FindConformingStep(objective, candidate_point, search_direction, step_size);
+
+                no_line_search_iterations += result.Iterations == 0 ? 1 : 0;
+                total_line_search_steps += result.Iterations;
+
+				step_size = (result.FunctionInfoAtMinimum.Point - candidate_point.Point).Norm (2.0);
+                candidate_point = result.FunctionInfoAtMinimum;
 
                 iterations += 1;
             }
 
-            return new MinimizationOutput(candidate_point, iterations);
+            return new MinimizationWithLineSearchOutput(candidate_point, iterations, total_line_search_steps, no_line_search_iterations);
         }
 
         private bool ExitCriteriaSatisfied(Vector<double> candidate_point, Vector<double> gradient)

src/Numerics/Optimization/LineSearchOutput.cs

@@ -0,0 +1,18 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+
+namespace MathNet.Numerics.Optimization
+{
+    public class LineSearchOutput : MinimizationOutput
+    {
+        public double FinalStep { get; private set; }
+
+        public LineSearchOutput(IEvaluation function_info, int iterations, double final_step)
+            : base(function_info, iterations)
+        {
+            this.FinalStep = final_step;
+        }
+    }
+}

src/Numerics/Optimization/LineSearchingMinimizerOutput.cs

@@ -0,0 +1,20 @@
+using System;
+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 IterationsWithNoLineSearch { get; private set; }
+
+        public MinimizationWithLineSearchOutput(IEvaluation function_info, int iterations, int total_line_search_iterations, int iterations_with_no_line_search)
+            : base(function_info, iterations)
+        {
+            this.TotalLineSearchIterations = total_line_search_iterations;
+            this.IterationsWithNoLineSearch = iterations_with_no_line_search;
+        }
+    }
+}

src/Numerics/Optimization/WeakWolfeLineSearch.cs

@@ -11,15 +11,17 @@ namespace MathNet.Numerics.Optimization
     {
         public double C1 { get; set; }
         public double C2 { get; set; }
+		public int MaximumIterations { get; set; }
 
-        public WeakWolfeLineSearch(double c1, double c2)
+        public WeakWolfeLineSearch(double c1, double c2, int max_iterations=10)
         {
             this.C1 = c1;
             this.C2 = c2;
+			this.MaximumIterations = max_iterations;
         }
 
         // Implemented following http://www.math.washington.edu/~burke/crs/408/lectures/L9-weak-Wolfe.pdf
-        public MinimizationOutput FindConformingStep(IObjectiveFunction objective, IEvaluation starting_point, Vector<double> search_direction, double initial_step)
+        public LineSearchOutput FindConformingStep(IObjectiveFunction objective, IEvaluation starting_point, Vector<double> search_direction, double initial_step)
         {
             double lower_bound = 0.0;
             double upper_bound = Double.PositiveInfinity;
@@ -32,7 +34,7 @@ namespace MathNet.Numerics.Optimization
 
             int ii;
             IEvaluation candidate_eval = null;
-            for (ii = 0; ii < 10; ++ii)
+            for (ii = 0; ii < this.MaximumIterations; ++ii)
             {
                 candidate_eval = objective.Evaluate(starting_point.Point + search_direction * step);
 
@@ -53,13 +55,21 @@ namespace MathNet.Numerics.Optimization
                     break;
                 }
             }
-
-            if (ii == 10)
+                       
+            if (ii == this.MaximumIterations)
                 throw new Exception("Line search failed with max iterations.  Function is likely unbounded in search direction.");
             else
-                return new MinimizationOutput(candidate_eval, ii);
+                return new LineSearchOutput(candidate_eval, ii, step);
         }
 
+        private bool Conforms(IEvaluation starting_point, Vector<double> search_direction, double step, IEvaluation ending_point)
+        {
+
+            bool sufficient_decrease = ending_point.Value <= starting_point.Value + this.C1 * step * (starting_point.Gradient * search_direction);
+            bool not_too_steep = ending_point.Gradient * search_direction >= this.C2 * starting_point.Gradient * search_direction;
+
+            return step > 0 && sufficient_decrease && not_too_steep;
+        }
 
     }
 }

src/UnitTests/OptimizationTests/TestConjugateGradientMinimizer.cs

@@ -17,16 +17,22 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
         public void FindMinimum_Rosenbrock_Easy()
         {
             var obj = new SimpleObjectiveFunction(RosenbrockFunction.Value, RosenbrockFunction.Gradient);
-            var solver = new ConjugateGradientMinimizer(1e-5, 100);
+            var solver = new ConjugateGradientMinimizer(1e-5, 1000);
             var result = solver.FindMinimum(obj, new MathNet.Numerics.LinearAlgebra.Double.DenseVector(new double[]{1.2,1.2}));
-            Assert.That(result.MinimizingPoint[0], Is.EqualTo(1.0));
-            Assert.That(result.MinimizingPoint[1], Is.EqualTo(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));
         }
 
         [Test]
         public void FindMinimum_Rosenbrock_Hard()
         {
+            var obj = new SimpleObjectiveFunction(RosenbrockFunction.Value, RosenbrockFunction.Gradient);
+            var solver = new ConjugateGradientMinimizer(1e-5, 1000);
+            var result = solver.FindMinimum(obj, new MathNet.Numerics.LinearAlgebra.Double.DenseVector(new double[] { -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));
         }
     }
 }