Explore cutting-edge research in discrete optimization through this comprehensive workshop featuring 28 recorded talks from leading experts in the field. Delve into continuous approaches to solving discrete optimization problems, covering topics from interior point methods and data structures to advanced algorithms for maximum flow, minimum cut, and submodular optimization. Learn about numerical analysis approaches to convex optimization, dynamic electrical flows, and novel techniques for approximating k-edge-connected spanning subgraphs. Discover applications in VLSI routing, cutting plane generation through sparse principal component analysis, and distributed algorithms for fair packing problems. Examine theoretical foundations including polynomial lower bounds for parallel submodular function minimization, dyadic linear programming, and learning-augmented online algorithms using the primal-dual method. Gain insights into practical implementations of interior point algorithms in column generation, local and global solutions for nonconvex quadratic problems, and adaptive gradient descent methods for constrained optimization. The workshop covers both theoretical advances and practical applications, making it valuable for researchers and practitioners working in optimization, algorithm design, and related computational fields.

Syllabus

Jan Van den Brand: From Interior Point Methods to Data Structures and back
Rasmus Kyng: A numerical analysis approach to convex optimization
Yang Liu: Fully Dynamic Electrical Flows: Sparse Maxflow Faster than Goldberg-Rao
Debmalya Panigrahi: Isolating Cuts: A New Tool for Minimum Cut Algorithms
Sorrachai Yingchareonthawornchai: Approximating k-Edge-Connected Spanning Subgraphs via a Fast [...]
Kent Quanrud: On Iterative Peeling and Supermodularity for Densest Subgraph
Sally Dong: Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time
Bento Natura: Fast Exact Solvers for Linear Programs via Interior Point Methods
Jacek Gondzio: Applying interior point algorithms in column generation and cuttingplane methods
Andrea Lodi: Cutting Plane Generation Through Sparse Principal Component Analysis
Jens Vygen: Continuous approaches to VLSI routing
Robert Luce: Local and global solution of nonconvex quadratic problems
Aaron Sidford: Unit Capacity Maximum Flow in Almost m^(4/3) Time
Rico Zenklusen, Vera Traub: Bridging the Gap Between Tree and Connectivity Augmentation
Matthias Mnich: Approximation Algorithms for Hard Cut Problems via Continuous Relaxations
Sebastian Pokutta: A distributed accelerated algorithm for the 1-fair packing problem
Stefan Weltge: Speeding up the Cutting Plane Method?
Zhao Song: Fast Iterative Algorithm via Nearest/Furthest Neighbor Search
Alina Ene: Adaptive gradient descent methods for constrained optimization
Jelena Diakonikolas: Local Acceleration of Frank-Wolfe Methods
Roie Levin: Random Order Set Cover is as Easy as Offline
Gerard Cornuejols: Dyadic linear programming
Ola Svensson: Learning-Augmented Online Algorithms and the Primal-Dual Method
Anupam Gupta: Covering LP Relaxations for k-Server
Sebastian Bubeck: Chasing small sets
Haotian Jiang: Minimizing Convex Functions with Integral Minimizers
Deeparnab Chakrabarty: Polynomial Lower Bounds for Parallel Submodular Function Minimization