4 citations · 4 across the 2 of their papers we have counts for
5 papers · 1 filter
Sparse SVM for Sufficient Data Reduction
Shenglong Zhou
Kernel-based methods for support vector machines (SVM) have shown highly advantageous performance in various applications. However, they may incur prohibitive computational costs f…
Theoretical and numerical comparison of the Karush-Kuhn-Tucker and value function reformulations in bilevel optimization
Alain Zemkoho, Shenglong Zhou
The Karush-Kuhn-Tucker and value function (lower-level value function, to be precise) reformulations are the most common single-level transformations of the bilevel optimization pr…
Semismooth Newton-type method for bilevel optimization: Global convergence and extensive numerical experiments
Andreas Fischer, Alain B. Zemkoho, Shenglong Zhou
We consider the standard optimistic bilevel optimization problem, in particular upper- and lower-level constraints can be coupled. By means of the lower-level value function, the p…
Support Vector Machine Classifier via Soft-Margin Loss
Huajun Wang, Yuanhai Shao, Shenglong Zhou +2
Support vector machine (SVM) has attracted great attentions for the last two decades due to its extensive applications, and thus numerous optimization models have been proposed. To…
BOLIB: Bilevel Optimization LIBrary of test problems
Shenglong Zhou, Alain B. Zemkoho, Andrey Tin
This chapter presents the Bilevel Optimization LIBrary of the test problems (BOLIB for short), which contains a collection of test problems, with continuous variables, to help supp…