We prove that gradient descent efficiently converges to the global optimizer of the maximum likelihood objective of an unknown linear time-invariant dynamical system from a sequence of noisy observations generated by the system. In spite of the obstacle that the objective function is non-convex, we provide polynomial running time and sample comp...
Creator:
Hardt, Moritz (Google Inc.)
Created:
2016-05-18
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
In this work we discuss an approach for uncertainty propagationthrough computationally expensive physics simulationcodes. Our approach incorporates gradient information informationto provide a higher quality surrogate with fewer simulationresults compared with derivative-free approaches.We use this information in two ways: we fit a polynomial or...
Creator:
Anitescu, Mihai (Argonne National Laboratory)
Created:
2011-06-02
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Our goal in this talk is to derive a method to compute surfaces that minimize general surface energies, in the form of weighted surface integrals with weights depending on the normal and the curvature of the surface. Energies of this form have applications in many areas, such as material science, biology and image processing. A well-known exampl...
Creator:
Dogan, Gunay (National Institute of Standards and Technology)
Created:
2013-07-16
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
(Joint work with Philipp Reiter.) Aiming at optimizing the shape of closed embedded curves within prescribed isotopy classes, we use a gradient-based approach to approximate stationary points of the Möbius energy. The gradients are computed with respect to certain fractional-order Sobolev scalar products that are adapted to the Möbius energy. I...
Creator:
Schumacher, Henrik (RWTH Aachen University)
Created:
2019-06-25
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Over the past ten years, optimal transport has become a fundamental tool in statistics and machine learning: the Wasserstein metric provides a new notion of distance for classifying distributions and a rich geometry for interpolating between them. In parallel, optimal transport has led to new theoretical results on the stability and long time be...
Creator:
Craig, Katy (University of California, Santa Barbara)
Created:
2020-11-13
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Built in 1901 on the University of Minnesota's St. Paul campus for Andrew Boss. In 1894, he had offered the first course in dressing and curing meats in an agricultural school in the U.S. or Canada.
Created:
1960-01-01
Contributed By:
University of Minnesota, Minnesota Agricultural Experiment Station.
Built in 1901 on the University of Minnesota's St. Paul campus for Andrew Boss. In 1894, he had offered the first course in dressing and curing meats in an agricultural school in the U.S. or Canada.
Created:
1960-07-01
Contributed By:
University of Minnesota, Minnesota Agricultural Experiment Station.
Built in 1901 on the University of Minnesota's St. Paul campus for Andrew Boss. In 1894, he had offered the first course in dressing and curing meats in an agricultural school in the U.S. or Canada.
Created:
1960-07-01
Contributed By:
University of Minnesota, Minnesota Agricultural Experiment Station.
Built in 1901 on the University of Minnesota's St. Paul campus for Andrew Boss. In 1894, he had offered the first course in dressing and curing meats in an agricultural school in the U.S. or Canada.
Created:
1960-07-01
Contributed By:
University of Minnesota, Minnesota Agricultural Experiment Station.
