English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Inverse linear programming (LP) has received increasing attention due to itspotential to generate efficient optimization formulations that can closelyreplicate the behavior of a complex system. However, inversely inferredparameters and corresponding forward solutions from the existing inverse LPmethod can be highly sensitive to noise, errors, and uncertainty in the inputdata, limiting its applicability in data-driven settings. We introduce thenotion of inverse and forward stability in inverse LP and propose a novelinverse LP method that determines a set of objective functions that are stableunder data imperfection and generate solutions close to the relevant subset ofthe data. We formulate the inverse model as a mixed-integer program andelucidate its connection to bi-clique problems, which we exploit to developefficient heuristics. We also show how this method can be used for onlinelearning. We numerically evaluate the stability of the proposed method anddemonstrate its practical use in the diet recommendation and transshipmentapplications.

Quantile Inverse Optimization: Improving Stability in Inverse Linear Programming