pyproximal.optimization.primal.ProximalPoint¶

pyproximal.optimization.primal.ProximalPoint(prox: ProxOperator, x0: ndarray[tuple[Any, ...], dtype[_ScalarT]], tau: float, niter: int = 10, tol: float | None = None, rtol: float | None = None, callback: Callable[[ndarray[tuple[Any, ...], dtype[_ScalarT]]], None] | None = None, show: bool = False, itershow: tuple[int, int, int] = (10, 10, 10)) → ndarray[tuple[Any, ...], dtype[_ScalarT]][source]¶

Proximal point algorithm

Solves the following minimization problem using Proximal point algorithm:

\[\mathbf{x} = \argmin_\mathbf{x} f(\mathbf{x})\]

where \(f(\mathbf{x})\) is any convex function that has a known proximal operator.

Parameters:

proxpyproximal.ProxOperator: Proximal operator
x0numpy.ndarray: Initial vector
taufloat: Positive scalar weight
niterint, optional: Number of iterations of iterative scheme
tolfloat, optional: Tolerance on change of objective function (used as stopping criterion). If tol=None, run until niter is reached or the other tolerance criterion is met
rtolfloat, optional: Relative tolerance on objective function wrt initial value. Stops the solver when the ratio of the current objective function to the initial objective function is below this value. If rtol=None, run until niter is reached or the other tolerance criterion is met
callbackcallable, optional: Function with signature (callback(x)) to call after each iteration where x is the current model vector
showbool, optional: Display iterations log
itershowtuple, optional: Display set log for the first N1 steps, last N2 steps, and every N3 steps in between where N1, N2, N3 are the three element of the list.

Returns:

Notes