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
Bazinga.jl: a toolbox for constrained composite optimization
Test Cases for Regularized 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
Asynchronous implementation of a Projective Splitting algorithm in Julia
Modeling language and tools for constrained, structured optimization problems
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