I am excited to chair two sessions at the 2024 @INFORMS Annual Meeting in Seattle! Join us to explore the latest advancements in optimization techniques and their applications. Session 1: Title: Advanced Optimization for Mixed Integer Programming Date/Time: Monday, October 21, 12:45 PM - 2:00 PM Venue: Regency - 709 Speakers: Peng Zhang, Stony Brook University - Enhancing Quantum Optimization Scalability: A Singular Transformation Approach to Unit Commitment Luke Marshall, Microsoft - Accelerating Branch-and-Price via Template Pricing Hubert Missbauer, University of Innsbruck - Using Lagrangian Decomposition to Coordinate Order Release Planning and Production Scheduling Mikhail Bragin, Southern California Edison - Acceleration of Level Adjustment for the Polyak Stepsize: Applications to Mixed Integer Programming Session 2: Title: Recent Advancements in Accelerated Optimization Date/Time: Tuesday, October 22, 4:00 PM - 5:15 PM Venue: Summit - 423 Speakers: Peijing Liu, University of Southern California - Polyhedral Analysis of Quadratic Optimization Problems with Stieltjes Matrices and Indicators Tao Jiang, Cornell University - A Linearly Convergent Gauss-Newton Subgradient Method for Ill-Conditioned Problems Xinyao Zhang, University of Southern California - Indefinite Quadratic Programs and Complementarity Constraints by a Progressive MIP Method Berkay Becu, Georgia Institute of Technology - A Machine Learning Approach for Rank-1 GMI Cuts Looking forward to deepening our collective understanding of optimization and its future directions! @informs2024 #INFORMS2024 #Optimization #MIP #MixedIntegerProgramming #AcceleratedOptimization #Research #OperationsResearch #QuantumOptimization

A relevant paper on the topic that you may find interesting: ieeexplore.ieee.org/stamp/st… inspired by the amazing work in link.springer.com/article/10… #AcceleratedOptimization #UCIMAE