iterreg.sparse.solvers.dual_primal

iterreg.sparse.solvers.dual_primal(X, y, max_iter=1000, f_store=10, prox=CPUDispatcher(<function shrink>), ret_all=True, callback=None, memory=10, step_ratio=1, rho=0.99, verbose=False)

Chambolle-Pock algorithm applied to the dual: interpolation on the primal update. Parameters ———- X : np.array, shape (n_samples, n_features)

Design matrix.

ynp.array, shape (n_samples,)

Observation vector.

max_iterint, optional (default=1000)

Maximum number of Chambolle-Pock iterations.

f_storeint, optional (default=10)

Primal iterates w are stored every f_store iterations.

proxcallable, optional (default=shrink)

Proximal operator of the minimized regularizer. By default, a shrink is used, corresponding to the convex L1 regularizer. It is given (w, tau) as input (the primal iterate and the primal stepsize).

ret_allbool, optional (default=True)

If True, return all stored primal iterates.

callbackcallable or None, optional (default=None)

Callable called on primal iterate w every f_store iterations.

memoryint, optional (default=10)

If callback is not None and its value did not decrease for the last memory stored iterates, the algorithm is early stopped.

step_ratiofloat, optional (default=1)

Ratio betwen primal and dual stepsizes (tau/sigma). If step_ratio=1, both stepsizes are equal.

rhofloat, optional (default=0.99)

The product of the step sizes is equal to rho / norm(X, ord=2)**2

verbosebool, optional (default=False)

Verbosity of the algorithm.

wnp.array, shape (n_features,)

Last or best primal iterate.

thetanp.array, shape (n_samples,)

Last or best dual iterate.

critsnp.array, shape (max_iter // f_store,)

Value of callback along iterations.

all_wnp.array, shape (max_iter // f_store, n_features)

Primal iterates every f_store iterations. Returned only if ret_all is True.