Recent topics in mixed-integer optimization

Liding Xu 徐立鼎

Zuse Institute Berlin

Liding Xu is a postdoctoral researcher in the Interactive Optimization and Learning (IOL) group led by Prof. Sebastian Pokutta at the Zuse Institute Berlin (ZIB). He received his Ph.D. from the LIX laboratory (CNRS) at École Polytechnique. He is a core developer of SCIP, one of the world’s fastest open-source solvers for mixed-integer nonlinear programming (MINLP). His research interests span global and nonconvex optimization, with applications in operations research and quantum information.

# Time

Thursday, 17:00-18:00

Dec. 11, 2025

# Venue

C654, Shuangqing Complex Building

Zoom Meeting ID: 271 534 5558

Passcode: YMSC

#Abstract

In this talk, I will share several research progress in mixed-integer optimization (MIO) that may influence the next generation MIO solvers. The first topic is a comparison between Google’s AlphaEvolve framework and the classical modeling–optimization pipeline. The second topic concerns GPU-accelerations for within MIP solvers. The third topic builds on the relationship between cutting planes, surrogate model, and enumeration. We explored the integration of the Fenchel-cut framework into SCIP solver, which was also named as local cuts in TSP context. Our preliminary results suggest that Fenchel cuts can be stronger than mixed-integer rounding cuts, although their generation is computationally expensive. These findings raise further theoretical questions, particularly regarding how to break the “single-row barrier” that underlies most modern cutting-plane systems.