[02374] Embarrassingly-parallel optimization algorithms for high-dimensional optimal control
Session Time & Room : 2E (Aug.22, 17:40-19:20) @F401
Type : Contributed Talk
Abstract : Developing efficient algorithms for Hamilton--Jacobi partial differential equations $(\text{HJ PDEs})$ is crucial for solving high-dimensional optimal control problems in real time but notoriously tricky due to the so-called curse of dimensionality. In this talk, we present novel grid-free and embarrassingly-parallel optimization algorithms for solving a broad class of HJ PDEs relevant to high-dimensional state-dependent optimal control problems. We illustrate their performance and efficiency on large-scale multi-agent path planning problems.
