More pathes from Tadasi Saito.

As discussed in ruby-dev ML:
  E,PI, etc are disabled.
  BigDecimal op String disabled.
  to_f changed.
  lib directory moved.


git-svn-id: svn+ssh://ci.ruby-lang.org/ruby/trunk@4092 b2dd03c8-39d4-4d8f-98ff-823fe69b080e

1 files changed, 0 insertions, 75 deletions

diff --git a/ext/bigdecimal/lib/newton.rb b/ext/bigdecimal/lib/newton.rb
deleted file mode 100644
index 67a92474ac..0000000000
--- a/ext/bigdecimal/lib/newton.rb
+++ /dev/null
@@ -1,75 +0,0 @@
-#
-# newton.rb 
-#
-# Solves nonlinear algebraic equation system f = 0 by Newton's method.
-#  (This program is not dependent on BigDecimal)
-#
-# To call:
-#    n = nlsolve(f,x)
-#  where n is the number of iterations required.
-#        x is the solution vector.
-#        f is the object to be solved which must have following methods.
-#
-#   f ... Object to compute Jacobian matrix of the equation systems.
-#       [Methods required for f]
-#         f.values(x) returns values of all functions at x.
-#         f.zero      returns 0.0
-#         f.one       returns 1.0
-#         f.two       returns 1.0
-#         f.ten       returns 10.0
-#         f.eps       convergence criterion
-#   x ... initial values
-#
-require "ludcmp"
-require "jacobian"
-
-module Newton
-  include LUSolve
-  include Jacobian
-  
-  def norm(fv,zero=0.0)
-    s = zero
-    n = fv.size
-    for i in 0...n do
-      s += fv[i]*fv[i]
-    end
-    s
-  end
-
-  def nlsolve(f,x)
-    nRetry = 0
-    n = x.size
-
-    f0 = f.values(x)
-    zero = f.zero
-    one  = f.one
-    two  = f.two
-    p5 = one/two
-    d  = norm(f0,zero)
-    minfact = f.ten*f.ten*f.ten
-    minfact = one/minfact
-    e = f.eps
-    while d >= e do
-      nRetry += 1
-      # Not yet converged. => Compute Jacobian matrix
-      dfdx = jacobian(f,f0,x)
-      # Solve dfdx*dx = -f0 to estimate dx
-      dx = lusolve(dfdx,f0,ludecomp(dfdx,n,zero,one),zero)
-      fact = two
-      xs = x.dup
-      begin
-        fact *= p5
-        if fact < minfact then
-          raize "Failed to reduce function values."
-        end
-        for i in 0...n do
-          x[i] = xs[i] - dx[i]*fact
-        end
-        f0 = f.values(x)
-        dn = norm(f0,zero)
-      end while(dn>=d)
-      d = dn
-    end
-    nRetry
-  end
-end