The PNPG approach employs projected Nesterov's acceleration step with restart and an inner iteration to compute the proximal mapping. We propose an adaptive step-size selection scheme to obtain a good local majorizing function of the NLL and reduce the time spent backtracking.
Apr 6, 2017 · Abstract: We develop a projected Nesterov's proximal-gradient (PNPG) approach for sparse signal reconstruction that combines adaptive step ...
Abstract—We develop a projected Nesterov's proximal-gradient. (PNPG) approach for sparse signal reconstruction that combines.
An integrated derivation of the momentum acceleration and proofs of the objective function convergence rate and convergence of the iterates, which account ...
(PDF) Projected Nesterov's Proximal-Gradient Algorithm for ...
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Oct 19, 2017 · We develop a projected Nesterov's proximal-gradient (PNPG) approach for sparse signal reconstruction that combines adaptive step size with ...
Projected Nesterov's Proximal-Gradient ... proximal-gradient algorithm for sparse signal reconstruction ... problems with applications to sparse signal recovery,” ...
In this paper, we propose a projected Nesterov's proximal-gradient (PNPG) method for sparse signal reconstruc- tion under this measurement scenario. We now ...
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... signal. recovery. Signal sparsity is imposed using the `1-norm penalty. on the signal's linear transform coefficients or gradient map,. respectively. The PNPG ...
Projected Nesterov's Proximal-Gradient Algorithms for Sparse Signal Reconstruction with a Convex Constraint · Summary · Download · Reference:.
