First-order variational analysis of non-amenable composite functions
Ashkan Mohammadi

TL;DR
This paper develops a first-order variational analysis framework for non-convex, non-differentiable functions using directional derivatives, enabling new algorithms for constrained optimization with convergence guarantees.
Contribution
It introduces exact chain and sum rules for non-amenable functions via subderivatives, facilitating auto-differentiation and first-order algorithms for complex optimization problems.
Findings
Established chain and sum rules for non-regular functions.
Proposed a first-order algorithm with $O( ext{epsilon}^{-2})$ convergence rate.
Identified semi-differentiability of distance functions for constraint sets.
Abstract
This paper is devoted to studying the first-order variational analysis of non-convex and non-differentiable functions that may not be subdifferentially regular. To achieve this goal, we entirely rely on two concepts of directional derivatives known as subderivative and semi-derivative. We establish the exact chain and sum rules for this class of functions via these directional derivatives. These calculus rules provide an implementable auto-differentiation process such as back-propagation in composite functions. The latter calculus rules can be used to identify the directional stationary points defined by the subderivative. We show that the distance function of a geometrically derivable constraint set is semi-differentiable, which opens the door for designing first-order algorithms for non-Clarke regular constrained optimization problems. We propose a first-order algorithm to find a…
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Taxonomy
TopicsOptimization and Variational Analysis · Advanced Optimization Algorithms Research · Sparse and Compressive Sensing Techniques
