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20202026
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math.OC2026

A single loop method for quadratic minmax optimization

Stefano Cipolla, Oliver Stein, Alain Zemkoho

We consider a quadratic minmax problem with coupled inner constraints and propose a method to compute a class of stationary points. To motivate the need to compute such stationary…

math.OC2025

On Constraint Qualifications for MPECs with Applications to Bilevel Hyperparameter Optimization for Machine Learning

Jiani Li, Qingna Li, Alain Zemkoho

Constraint qualifications for a Mathematical Program with Equilibrium Constraints (MPEC) are essential for analyzing stationarity properties and establishing convergence results. I…

math.OC2023

Global relaxation-based LP-Newton method for multiple hyperparameter selection in support vector classification with feature selection

Yaru Qian, Qingna Li, Alain Zemkoho

Support vector classification (SVC) is an effective tool for classification tasks in machine learning. Its performance relies on the selection of appropriate hyperparameters. This…

math.OC2022

A basic time series forecasting course with Python

Alain Zemkoho

The aim of this paper is to present a set of Python-based tools to develop forecasts using time series data sets. The material is based on a four week course that the author has ta…

math.OC2021

Bilevel hyperparameter optimization for support vector classification: theoretical analysis and a solution method

Qingna Li, Zhen Li, Alain Zemkoho

Support vector classification (SVC) is a classical and well-performed learning method for classification problems. A regularization parameter, which significantly affects the class…

math.OC2020

Gauss-Newton-type methods for bilevel optimization

Joerg Fliege, Andrey Tin, Alain Zemkoho

This article studies Gauss-Newton-type methods for over-determined systems to find solutions to bilevel programming problems. To proceed, we use the lower-level value function refo…