Code and Data Repository for Asymptotically Tight MILP Approximations for a Non-convex QCP

The goal of this repository is to share data and results related to the non-convex quadratically constrained program (QCP), solved using our asymptotically tight mixed-integer linear programming (MILP) approximations.

Authors

Jianqiang Cheng (ORCID: https://orcid.org/0000-0003-3358-9176)
Boshi Yang (ORCID: https://orcid.org/0000-0001-6760-9054)
Kai Pan (ORCID: https://orcid.org/0000-0003-2127-5348)
Shiyi Jiang (ORCID: https://orcid.org/0000-0002-9766-3518)

Publication Details

Journal: INFORMS journal on computing
Published: 2025-04-09
DOI: https://doi.org/10.1287/ijoc.2024.0719.cd
Primary Topic: Blind Source Separation Techniques
Type: article
Field-Weighted Citation Impact: 0.00

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article

Code and Data Repository for Asymptotically Tight MILP Approximations for a Non-convex QCP

Jianqiang Cheng, Boshi Yang, Kai Pan, Shiyi Jiang

Apr 9, 2025

INFORMS journal on computing

Blind Source Separation Techniques

article

Code and Data Repository for Asymptotically Tight MILP Approximations for a Non-convex QCP

Jianqiang Cheng, Boshi Yang, Kai Pan, Shiyi Jiang

April 9, 2025article en

Abstract

INFORMS journal on computing

Openalex Percentile: Top 92%

Blind Source Separation Techniques

DOI Source OpenAlex

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Code and Data Repository for Asymptotically Tight MILP Approximations for a Non-convex QCP — Jianqiang Cheng, Boshi Yang, et al. · INFORMS journal on computing (2025) | TGRS Research Map | TGRS