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.
