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June 5, 2026
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
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Restricted inverse optimal value problem on linear programming under weighted l1 norm

Journal of Computational and Applied Mathematics. 2026. Vol. 486. Article 117687.
Jia J., Guan X., Pardalos P. M.

We study the restricted inverse optimal value problem on linear programming under weighted l1 norm (RIOVLP1). Given a linear programming problem (LP with a feasible solution x0 and a value K, we aim to adjust the cost vector c to  such that x0 becomes an optimal solution of the problem (LP) whose objective value  equals K. The objective function is to minimize the distance  under weighted l1 norm. First, we reformulate the problem (RIOVLP1) as a linear programming problem by duality theory. Then, we construct a sub-problem which has similar form as (LPc). Next, when the coefficient matrix A is unimodular, we propose a binary search algorithm to calculate the optimal solution of the problem (RIOVLP1) in each iteration we solve a sub-problem. Finally, we apply the research methods to solve the restricted inverse optimal value problems on Hitchcock and shortest path problem under weighted l1 norm, respectively.

Research target: Computer Science
Language: English
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Keywords: Linear programmingInverse optimization probleml1 normCombinatorial OptimizationRestricted inverse optimal value problem
Publication based on the results of:
Network and graph models, algorithmic complexity and data mining (2025)
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