Work

A sequential quadratic programming method for optimization with expectation objective functions, deterministic inequality constraints and robust subproblems

Year: 2026

Type: article

Abstract: In this paper we generalize a robust sequential quadratic programming method for constrained optimization to problems involving stochastic objective functions, which are characterized by expectation operations and subject to deterministic equality and inequality constraints. The proposed algorithm incorporates a stochastic line-search strategy to ensure global convergence. Under the extended Mangasarian–Fromovitz constraint qualification we establish the almost sure convergence of the generated sequences to Karush–Kuhn–Tucker points. Numerical experiments demonstrate the effectiveness of the proposed method.

Source: IMA Journal of Numerical Analysis

DOI: 10.1093/imanum/drag073

Authors: Songqiang Qiu, Vyacheslav Kungurtsev

Institutions: China University of Mining and Technology, Czech Technical University in Prague

Language: English

Topic: Advanced Optimization Algorithms Research

Subfield: Numerical Analysis

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, China Scholarship Council, Research Center for Informatics, Czech Technical University in Prague, HORIZON EUROPE Framework Programme

Awards: COMBATTING DIET RELATED NON-COMMUNICABLE DISEASE THROUGH ENHANCED SURVEILLANCE, G8072696303, G9000368281