diff options
Diffstat (limited to 'jni/ruby/ext/bigdecimal/lib/bigdecimal/newton.rb')
-rw-r--r-- | jni/ruby/ext/bigdecimal/lib/bigdecimal/newton.rb | 79 |
1 files changed, 79 insertions, 0 deletions
diff --git a/jni/ruby/ext/bigdecimal/lib/bigdecimal/newton.rb b/jni/ruby/ext/bigdecimal/lib/bigdecimal/newton.rb new file mode 100644 index 0000000..db1a5ad --- /dev/null +++ b/jni/ruby/ext/bigdecimal/lib/bigdecimal/newton.rb @@ -0,0 +1,79 @@ +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 |