English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Wasserstein \textbf{D}istributionally \textbf{R}obust \textbf{O}ptimization(DRO) is concerned with finding decisions that perform well on data that aredrawn from the worst-case probability distribution within a Wasserstein ballcentered at a certain nominal distribution. In recent years, it has been shownthat various DRO formulations of learning models admit tractable convexreformulations. However, most existing works propose to solve these convexreformulations by general-purpose solvers, which are not well-suited fortackling large-scale problems. In this paper, we focus on a family ofWasserstein distributionally robust support vector machine (DRSVM) problems andpropose two novel epigraphical projection-based incremental algorithms to solvethem. The updates in each iteration of these algorithms can be computed in ahighly efficient manner. Moreover, we show that the DRSVM problems consideredin this paper satisfy a H\"olderian growth condition with explicitly determinedgrowth exponents. Consequently, we are able to establish the convergence ratesof the proposed incremental algorithms. Our numerical results indicate that theproposed methods are orders of magnitude faster than the state-of-the-art, andthe performance gap grows considerably as the problem size increases.

Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine