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[00302] Manifold-Free Riemannian Optimization

  • Session Time & Room : 3C (Aug.23, 13:20-15:00) @E605
  • Type : Contributed Talk
  • Abstract : Optimization problems constrained to a smooth manifold can be solved via the framework of Riemannian optimization. To that end, a geometrical description of the constraining manifold, e.g., tangent spaces, retractions, and cost function gradients, is required. In this talk, we present a novel approach that allows performing approximate Riemannian optimization based on a manifold learning technique, in cases where only a noiseless sample set of the cost function and the manifold’s intrinsic dimension are available.
  • Classification : 65K05, 53Z50, 65D15
  • Format : Talk at Waseda University
  • Author(s) :
    • Boris Shustin (Tel-Aviv University)
    • Haim Avron (Tel-Aviv University)
    • Barak Sober (The Hebrew University of Jerusalem)