1 citations · 1 across the 8 of their papers we have counts for
10 papers
A true single-level reformulation for pessimistic bilevel optimization
Oliver Stein, Alain Zemkoho
We propose a single-level reformulation (SLR) for the pessimistic bilevel optimization problem that does not rely on complementarity conditions or optimal value functions. For this…
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…
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…
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…
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…
Extension of the value function reformulation to multiobjective bilevel optimization
Lahoussine Lafhim, Alain Zemkoho
We consider a multiobjective bilevel optimization problem with vector-valued upper- and lower-level objective functions. Such problems have attracted a lot of interest in recent ye…
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…
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…
A note on partial calmness for bilevel optimization problems with linearly structured lower level
Patrick Mehlitz, Leonid I. Minchenko, Alain B. Zemkoho
Partial calmness is a celebrated but restrictive property of bilevel optimization problems whose presence opens a way to the derivation of Karush--Kuhn--Tucker-type necessary optim…
Estimates of generalized Hessians for optimal value functions in mathematical programming
Alain B. Zemkoho
The optimal value function is one of the basic objects in the field of mathematical optimization, as it allows the evaluation of the variations in the cost/revenue generated while…