Test Cases for Regularized Optimization
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Updated
Jun 3, 2024 - Julia
Test Cases for Regularized Optimization
Proximal algorithms for nonsmooth optimization in Julia
Proximal operators for nonsmooth optimization in Julia
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.
Modeling language and tools for constrained, structured optimization problems
A Julia package that solves Linearly Constrained Separable Optimization Problems using ADMM.
A Julia package for manipulation of univariate piecewise quadratic functions.
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