曾与蒿藜同雨露,한때 잡초와 쑥과 함께 비와 이슬을 나누던 곳이 이제는 소나무와 삼나무와 함께 서리와 눈을 견뎌내고 있다.终随松柏到冰霜.かつては雑草やヨモギと共に雨や露を分かち合っていたが、今では松やヒノキと共に霜や雪に耐えている。曾与蒿藜同雨露,Once sharing rain and dew with weeds and wormwood, now enduring frost and snow with pines and cypresses.终随松柏到冰霜.曾与蒿藜同雨露한때 잡초와 쑥과 함께 비와 이슬을 나누던 곳이 이제는 소나무와 삼나무와 함께 서리와 눈을 견뎌내고 있다.,终随松柏到冰霜.譖セ荳手珍阯懷酔髮ィ髴イ�檎サ磯囂譚セ譟丞芦蜀ー髴�曾与蒿藜同雨露,鏇句笌钂胯棞鍚岄洦闇诧紝缁堥殢鏉炬煆鍒板啺闇�终随松柏到冰霜.曾与蒿藜同雨露,한때 잡초와 쑥과 함께 비와 이슬을 나누던 곳이 이제는 소나무와 삼나무와 함께 서리와 눈을 견뎌내고 있다.终随松柏到冰霜.曾与蒿藜同雨露,终随松柏到冰霜. rahbord-ins.ir - GrazzMean-Shell
shell bypass 403

GrazzMean-Shell Shell

: /opt/alt/ruby21/lib64/ruby/2.1.0/bigdecimal/ [ drwxr-xr-x ]
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show_source, system, shell_exec, passthru, exec, popen, proc_open

name : newton.rb
require "bigdecimal/ludcmp"
require "bigdecimal/jacobian"

#
# newton.rb
#
# Solves the 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 initial value vector
#        f is an Object which is used to compute the values of the equations to be solved.
# It must provide the following methods:
#
# f.values(x):: returns the values of all functions at x
#
# f.zero:: returns 0.0
# f.one:: returns 1.0
# f.two:: returns 2.0
# f.ten:: returns 10.0
#
# f.eps:: returns the convergence criterion (epsilon value) used to determine whether two values are considered equal. If |a-b| < epsilon, the two values are considered equal.
#
# On exit, x is the solution vector.
#
module Newton
  include LUSolve
  include Jacobian
  module_function

  def norm(fv,zero=0.0) # :nodoc:
    s = zero
    n = fv.size
    for i in 0...n do
      s += fv[i]*fv[i]
    end
    s
  end

  # See also Newton
  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
          raise "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
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