![]() Some further reading and related software: ĭ.A. Lot more depth to this topic than is shown here. In making a simple choice that worked reasonably well, but there is a Preconditioning is an art, science, and industry. it can even decide whether the problem is solvable in practice or Residual is expensive to compute, good preconditioning can be crucial Using a preconditioner reduced the number of evaluations of the x def main (): sol = solve ( preconditioning = True ) # visualize import matplotlib.pyplot as plt x, y = mgrid plt. Find root of the equation x cos(x) : from scipy. max () print 'Evaluations', count return sol. #Scipy optimize example licenseSource File: value_iteration.py View license def solve_policy(model, tol=1e-6, grid= |'.
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