seperate classes added for 2 conjugate gradient methods:Polak Ribbiere, Fletcher Reeves

Author: lasandun

lib/multidim/conjugate_gradient.rb

@@ -4,19 +4,8 @@
 
 module Minimization
 
-  # = Conjugate Gradient minimizer.
-  # A multidimensional minimization methods.
-  # == Usage.
-  #  require 'minimization'
-  #  f  = proc{ |x| (x[0] - 2)**2 + (x[1] - 5)**2 + (x[2] - 100)**2 }
-  #  fd = proc{ |x| [ 2 * (x[0] - 2) , 2 * (x[1] - 5) , 2 * (x[2] - 100) ] }
-  #  min = Minimization::NonLinearConjugateGradientMinimizer.new(f, fd, [0, 0, 0], :polak_ribiere)
-  #  while(min.converging?)
-  #    min.minimize
-  #  end
-  #  min.x_minimum
-  #  min.f_minimum
-  #
+  # Conjugate Gradient minimizer class
+  # The beta function may be :fletcher_reeves or :polak_ribiere
   class NonLinearConjugateGradientMinimizer
 
     attr_reader :x_minimum
@@ -188,4 +177,44 @@ def minimize
       end
     end
   end
+  
+  
+  # = Conjugate Gradient Fletcher Reeves minimizer.
+  # A multidimensional minimization methods.
+  # == Usage.
+  #  require 'minimization'
+  #  f  = proc{ |x| (x[0] - 2)**2 + (x[1] - 5)**2 + (x[2] - 100)**2 }
+  #  fd = proc{ |x| [ 2 * (x[0] - 2) , 2 * (x[1] - 5) , 2 * (x[2] - 100) ] }
+  #  min = Minimization::FletcherReeves.new(f, fd, [0, 0, 0])
+  #  while(min.converging?)
+  #    min.minimize
+  #  end
+  #  min.x_minimum
+  #  min.f_minimum
+  #
+  class FletcherReeves < NonLinearConjugateGradientMinimizer
+    def initialize(f, fd, start_point)
+      super(f, fd, start_point, :fletcher_reeves)
+    end
+  end
+
+  # = Conjugate Gradient Polak Ribbiere minimizer.
+  # A multidimensional minimization methods.
+  # == Usage.
+  #  require 'minimization'
+  #  f  = proc{ |x| (x[0] - 2)**2 + (x[1] - 5)**2 + (x[2] - 100)**2 }
+  #  fd = proc{ |x| [ 2 * (x[0] - 2) , 2 * (x[1] - 5) , 2 * (x[2] - 100) ] }
+  #  min = Minimization::PolakRibiere.new(f, fd, [0, 0, 0])
+  #  while(min.converging?)
+  #    min.minimize
+  #  end
+  #  min.x_minimum
+  #  min.f_minimum
+  #
+  class PolakRibiere < NonLinearConjugateGradientMinimizer
+    def initialize(f, fd, start_point)
+      super(f, fd, start_point, :polak_ribiere)
+    end
+  end
+
 end

spec/minimization_conjugate_gradient_fletcher_reeves.rb

@@ -1,6 +1,6 @@
 require "./../lib/multidim/conjugate_gradient.rb"
 
-describe Minimization::NonLinearConjugateGradientMinimizer do
+describe Minimization::PolakRibiere do
 
   before :all do
     @n           = 3
@@ -20,7 +20,7 @@
     # fletcher_reeves example 1
     f  = proc{ |x| (x[0] - @p[0])**2 + (x[1] - @p[1])**2 + (x[2] - @p[2])**2 }
     fd = proc{ |x| [ 2 * (x[0] - @p[0]) , 2 * (x[1] - @p[1]) , 2 * (x[2] - @p[2]) ] }
-    @min1 = Minimization::NonLinearConjugateGradientMinimizer.new(f, fd, @start_point, :fletcher_reeves)
+    @min1 = Minimization::PolakRibiere.new(f, fd, @start_point)
     while(@min1.converging?)
       @min1.minimize
     end
@@ -34,7 +34,7 @@
             r2 = ( @p[0]*x[0] + @p[1]*x[1] + @p[2]*x[2] ) * 2 * @p[2]
             [r0, r1, r2]
           }
-    @min2 = Minimization::NonLinearConjugateGradientMinimizer.new(f2, fd2, @start_point, :fletcher_reeves)
+    @min2 = Minimization::PolakRibiere.new(f2, fd2, @start_point)
     while(@min2.converging?)
       @min2.minimize
     end
@@ -43,7 +43,7 @@
     f3  = proc{ |x| ( (x[0] - @p[0])**2 + @k ) }
     fd3 = proc{ |x| [ (x[0] - @p[0]) * 2 ] }
     starting_point_3 = [rand(@limit)]
-    @min3 = Minimization::NonLinearConjugateGradientMinimizer.new(f3, fd3, starting_point_3, :fletcher_reeves)
+    @min3 = Minimization::PolakRibiere.new(f3, fd3, starting_point_3)
     while(@min3.converging?)
       @min3.minimize
     end

spec/minimization_conjugate_gradient_polak_ribiere.rb

@@ -1,6 +1,6 @@
 require "./../lib/multidim/conjugate_gradient.rb"
 
-describe Minimization::NonLinearConjugateGradientMinimizer do
+describe Minimization::PolakRibiere do
 
   before :all do
     @n           = 3
@@ -20,7 +20,7 @@
     # example 1
     f  = proc{ |x| (x[0] - @p[0])**2 + (x[1] - @p[1])**2 + (x[2] - @p[2])**2 }
     fd = proc{ |x| [ 2 * (x[0] - @p[0]) , 2 * (x[1] - @p[1]) , 2 * (x[2] - @p[2]) ] }
-    @min1 = Minimization::NonLinearConjugateGradientMinimizer.new(f, fd, @start_point, :polak_ribiere)
+    @min1 = Minimization::PolakRibiere.new(f, fd, @start_point)
     while(@min1.converging?)
       @min1.minimize
     end
@@ -30,7 +30,7 @@
     f2  = proc{ |x| ( (x[0] - @p[0])**2 + @k ) }
     fd2 = proc{ |x| [ (x[0] - @p[0]) * 2 ] }
     starting_point_2 = [rand(@limit)]
-    @min2 = Minimization::NonLinearConjugateGradientMinimizer.new(f2, fd2, starting_point_2, :polak_ribiere)
+    @min2 = Minimization::PolakRibiere.new(f2, fd2, starting_point_2)
     while(@min2.converging?)
       @min2.minimize
     end