pylat/adaptive-proximal-algorithms
A Julia package for adaptive proximal gradient and primal-dual algorithms
This is a Julia package designed for researchers and practitioners working with convex optimization problems, particularly those involving large datasets or complex models. It provides adaptive proximal gradient and primal-dual algorithms that can efficiently solve these problems. You input your specific convex optimization problem, and it outputs an optimized solution along with performance plots.
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Use this if you are a researcher or a quantitative analyst who needs to solve convex optimization problems efficiently, especially when dealing with non-smooth functions or large-scale data where standard methods are too slow.
Not ideal if you are looking for a general-purpose optimization library for simple problems or if you are not familiar with convex optimization theory.
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Jan 18, 2024
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