Proximal operators for nonsmooth optimization in Julia
Proximal algorithms for nonsmooth optimization in Julia
A Julia package that solves Linearly Constrained Separable Optimization Problems using ADMM.
Newton-type accelerated proximal gradient method in Julia
Coordinate and Incremental Aggregated Optimization Algorithms
Proximal operators for use with RegularizedOptimization
Test Cases for Regularized Optimization
Bazinga.jl: a toolbox for constrained composite optimization
Provides proximal operator evaluation routines and proximal optimization algorithms, such as (accelerated) proximal gradient methods and alternating direction method of multipliers (ADMM), for non-smooth/non-differentiable objective functions.
Self-concordant Smoothing for Large-Scale Convex Composite Optimization
Modeling language and tools for constrained, structured optimization problems
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