boxmin_amoeba(objfunc,
x0,
bounds,
ftol=1e-10,
xtol=1e-10,
maxiter=10000,
maxfun=10000,
restart=0,
full_output=1)
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Minimize a function using the downhill simplex(dhs) algorithm with
the box bound constrains.
- Description:
- Uses a Nelder-Mead simplex(Amoeba) algorithm to find the minimum of
function of one or more variables with the [low, high] bound
- Inputs:
- func: the Python function or method to be minimized.
x0: the initial guess
bound: the box boundary, it is a list of (lowBounds, highBounds)
xtol: acceptable relative error in xopt for convergence.
ftol: acceptable relative error in func(xopt) for convergence.
maxiter: the maximum number of iterations to perform.
maxfun: the maximum number of function evaluations.
full_output: non-zero if fval and warnflag outputs are desired.
- Outputs: (xopt, {fopt, iter, funcalls, warnflag})
- xopt: minimizer of function
fopt: value of function at minimum: fopt = func(xopt)
iter: number of iterations
funcalls: number of function calls
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