6 papers · 1 filter
Piecewise smooth functions and conservative fields: calculus for nonsmooth nonconvex optimization beyond stratification
Cheik Traoré
In this paper, we show that the piecewise gradient associated with a representation of a continuous piecewise- function is a selection of a conservative field. Specifically,…
The adjoint state method for parametric definable optimization without smoothness or uniqueness
Jérôme Bolte, Edouard Pauwels, Cheik Traoré
We establish that nonconvex definable parametric optimization problems with possibly nonsmooth objectives, inequality constraints, conic constraint systems, and non-unique primal a…
Bregman Stochastic Proximal Point Algorithm with Variance Reduction
Cheik Traoré, Peter Ochs
Stochastic algorithms, especially stochastic gradient descent (SGD), have proven to be the go-to methods in data science and machine learning. In recent years, the stochastic proxi…
A Proximal Stochastic Variance Reduced Zeroth-order Algorithm using Structured Finite-Differences
Marco Rando, Cheik Traoré, Cesare Molinari +2
Minimizing finite sums of functions is a central problem in optimization, arising in numerous practical applications. Such problems are commonly addressed using first-order optimiz…
Variance reduction techniques for stochastic proximal point algorithms
Cheik Traoré, Vassilis Apidopoulos, Saverio Salzo +1
In the context of finite sums minimization, variance reduction techniques are widely used to improve the performance of state-of-the-art stochastic gradient methods. Their practica…
Convergence of an Asynchronous Block-Coordinate Forward-Backward Algorithm for Convex Composite Optimization
Cheik Traoré, Saverio Salzo, Silvia Villa
In this paper, we study the convergence properties of a randomized block-coordinate descent algorithm for the minimization of a composite convex objective function, where the block…