Global sensitivity analysis and robustness in linear programming using different norms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10509906" target="_blank" >RIV/00216208:11320/25:10509906 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=l0fOmehkWZ" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=l0fOmehkWZ</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10100-025-00960-5" target="_blank" >10.1007/s10100-025-00960-5</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Global sensitivity analysis and robustness in linear programming using different norms

Popis výsledku v původním jazyce

Sensitivity analysis in linear programming is a standard technique for measuring the effects of variations in one coefficient on the optimal value and optimal solution. However, one-coefficient variations are too simple to reflect the complexity of real-life situations. That is why we propose a more general approach and consider variations of possibly all input data. The goal is to determine the maximum variations of the data in a given norm such that the computed optimal basis remains optimal. We present general results valid for an arbitrary norm, and then we focus particularly on the spectral norm and the maximum norm. Further, we analyse computational complexity of the problem, and for the computationally hard cases we derive efficiently computable lower and upper bounds. Besides the basic case, in which we allow variations of all input coefficients, we also consider variations of certain submatrices or along a certain pattern. Eventually, we present results of numerical experiments, where we analysed and compared computation time and accuracy of the proposed approximations on a collection of dataset.

Název v anglickém jazyce

Global sensitivity analysis and robustness in linear programming using different norms

Popis výsledku anglicky

Sensitivity analysis in linear programming is a standard technique for measuring the effects of variations in one coefficient on the optimal value and optimal solution. However, one-coefficient variations are too simple to reflect the complexity of real-life situations. That is why we propose a more general approach and consider variations of possibly all input data. The goal is to determine the maximum variations of the data in a given norm such that the computed optimal basis remains optimal. We present general results valid for an arbitrary norm, and then we focus particularly on the spectral norm and the maximum norm. Further, we analyse computational complexity of the problem, and for the computationally hard cases we derive efficiently computable lower and upper bounds. Besides the basic case, in which we allow variations of all input coefficients, we also consider variations of certain submatrices or along a certain pattern. Eventually, we present results of numerical experiments, where we analysed and compared computation time and accuracy of the proposed approximations on a collection of dataset.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA25-15714S" target="_blank" >GA25-15714S: Pokročilá teorie robustnosti v operačním výzkumu a optimalizačních modelech</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Central European Journal of Operations Research

ISSN

1435-246X

e-ISSN

1613-9178

Svazek periodika

33

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

17

Strana od-do

661-677

Kód UT WoS článku

001410547700001

EID výsledku v databázi Scopus

2-s2.0-85217436497