This paper considers distributed stochastic optimization, in which a number of agents
cooperate to optimize a global objective function through local computations and information …

Abstract The Frank-Wolfe algorithm is a popular method in structurally constrained machine
learning applications, due to its fast per-iteration complexity. However, one major limitation …

This paper proposes a distributed stochastic projection-free algorithm for large-scale
constrained finite-sum optimization whose constraint set is complicated such that the …

The Frank-Wolfe method is a popular method in sparse constrained optimization, due to its
fast per-iteration complexity. However, the tradeoff is that its worst case global convergence …

The Frank Wolfe algorithm (FW) is a popular projection-free alternative for solving large-
scale constrained optimization problems. However, the FW algorithm suffers from a …

Conic programming has well-documented merits in a gamut of signal processing and
machine learning tasks. This contribution revisits a recently developed first-order conic …

Modern machine learning tasks built to learn from data can be typically formulated as
optimization problems. The large volume of data justifies the pressing need for efficient and …

The Frank Wolfe algorithm (FW) is a popular projection-free alternative for solving large-
scale constrained optimization problems. However, the FW algorithm suffers from a …

Single-cell RNA sequencing (scRNA-seq) and spatially-resolved imaging/sequencing
technologies are the current cutting edge of transcriptomics data generation in biomedical …