Data Structures and Optimization for Fast Algorithms
summary
Program surveys concrete advances that shave decades off runtimes for tasks like linear system solving, convex programming, flow and Markov decision process solvers, with several results claiming nearly linear time provable bounds. It explains which optimization methods are used (gradient descent, interior point, multiplicative weights), which data structures and graph decompositions enable them, and how sketching techniques and dynamization turn optimization into maintainable, fast updates. Videos of the workshops are available.
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