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Readme
This optimizer varies a list of input variables x1, x2, ... xn in order to find a minimum for an target function func(x1, x2, ...xn).
The optimizer supports min/max limits of the input variables and inputs that can only be integers.
The optimization process either starts at a given point of initial input values or does a grid search in the input space to increase the probability to find a global optimum instead of a local one. It supports an automatic learning rate adjustment via backtracking line search, autmatic zoom-in for fine tuning as well as an adjustable momentum.
Using the optimizer involves four steps:
Parameter (name, initial_value=None, min=None, max=None, grid=None, integral=False, dx=None)
The meaning of these parameters:
name: identifies the parameter
initial_value: where the search will start. Either initial_value or a grid must be specified
min/max: limits for this input
grid: an integer specifying the number of intervals between min and max. The midpoint in each of these intervals will be a start point for optimization. If multiple parameters have grids, their cartesian product will be used.
integral: if True, the input is an integer
dx: the (initial) increment to estimate the gradient at a certain point by (f(x + dx) - f(x)) / dx
Must be specified if initial_value is zero. Otherwise, dx defaults to initial_value * 0.001
Optimizer (function, args='enum', zoom_limit=1e6, cfactor=0.5, momentum=0, iterations=100, min_improvement=0, trace=False, debug=False)
The meaning of these parameters:
opt.add_par (param1)
opt.add_par (param2)
...
res = opt.optimize()
# res[0] will contain a dictionary of the optimal parameter values
# res[1] will conmtain the function value at this point
FAQs
optimization of multivariate functions by gradient descent
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