Proximal algorithms for nonsmooth optimization in Julia
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Updated
Oct 1, 2024 - Julia
Proximal algorithms for nonsmooth optimization in Julia
Iterative shrinkage / thresholding algorithms (ISTAs) for linear inverse problems
A Julia package for adaptive proximal gradient and primal-dual algorithms
Test Cases for Regularized Optimization
Accelerated Proximal Gradient (APG) algorithm implementation for Nuclear Norm regularized linear Least Squares problem (NNLS).
Proximal Gradient/Semismooth Newton Methods for Projection onto a Polyhedron via the Duality-Gap-Active-Set Strategy
Implementation and brief comparison of different First Order and different Proximal gradient methods, comparison of their convergence rates
[Optimization algorithms] Study of the Proximal Gradient Method, Stochastic Gradient Descent method and Adam optimizer
Approximate Bregman proximal gradient algorithm
Regularized methods for efficient ranking in networks
Unified implementation of MGProx.
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