Convergence of Proximal Gradient Algorithm in the Presence of Adjoint Mismatch

combination of morphological characters, e.g., switch from decussate to spiral phyllotaxis with 90-100 ° divergence, combined with a change from interpetiolar to lateral

Our fault-tolerant PCG versions thus have the following ingredients: ABFT to detect up to two errors in the SpMxV and correct the error, if there was only one; TMR for

The latter is close to our subject since ISTA is in fact an instance of PGM with the L 2 -divergence – and FISTA [Beck and Teboulle, 2009] is analogous to APGM with the L 2

We consider the approximation of the ground state of the one-dimensional cubic nonlinear Schr¨ odinger equation by a normalized gradient algorithm combined with linearly implicit

In this paper, we proposed a novel framework for variable metric (quasi- Newton) forward–backward splitting algorithms, designed to efficiently solve non-smooth convex

Despite the difficulty in computing the exact proximity operator for these regularizers, efficient methods have been developed to compute approximate proximity operators in all of

finite number of iterations (finite activity identification), and (ii) then enters a local linear convergence 9.. regime, which we characterize precisely in terms of the structure

In doing so, we put to the fore inertial proximal algorithms that converge for general monotone inclusions, and which, in the case of convex minimization, give fast convergence rates