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Imports: copy, dot, sqrt, cubic_ext, quadratic_fafbga, quadratic
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A line search algorithm implemented using the strong Wolfe conditions. Algorithm 3.2, page 59, from 'Numerical Optimization' by Jorge Nocedal and Stephen J. Wright, 1999, 2nd ed. Requires the gradient function. ####################################################################################### These functions require serious debugging and recoding to work properly (especially the safeguarding). ####################################################################################### Function options ~~~~~~~~~~~~~~~~ func - The function to minimise. func_prime - The function which returns the gradient vector. args - The tuple of arguments to supply to the functions func and dfunc. x - The parameter vector at minimisation step k. f - The function value at the point x. g - The function gradient vector at the point x. p - The descent direction. a_init - Initial step length. a_max - The maximum value for the step length. mu - Constant determining the slope for the sufficient decrease condition (0 < mu < eta < 1). eta - Constant used for the Wolfe curvature condition (0 < mu < eta < 1). |
Find the minimum function value in the open interval (a_lo, a_hi) Algorithm 3.3, page 60, from 'Numerical Optimization' by Jorge Nocedal and Stephen J. Wright, 1999, 2nd ed. |
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