Zobrazeno 1 - 10
of 859
pro vyhledávání: '"Karlsson Johan"'
We study multi-marginal optimal transport (MOT) problems where the underlying cost has a graphical structure. These graphical multi-marginal optimal transport problems have found applications in several domains including traffic flow control and regr
Externí odkaz:
http://arxiv.org/abs/2406.10849
We introduce an efficient computational framework for solving a class of multi-marginal martingale optimal transport problems, which includes many robust pricing problems of large financial interest. Such problems are typically computationally challe
Externí odkaz:
http://arxiv.org/abs/2406.09959
Clustering is one of the most fundamental tools in data science and machine learning, and k-means clustering is one of the most common such methods. There is a variety of approximate algorithms for the k-means problem, but computing the globally opti
Externí odkaz:
http://arxiv.org/abs/2402.13595
Globally solving the Gromov-Wasserstein problem for point clouds in low dimensional Euclidean spaces
This paper presents a framework for computing the Gromov-Wasserstein problem between two sets of points in low dimensional spaces, where the discrepancy is the squared Euclidean norm. The Gromov-Wasserstein problem is a generalization of the optimal
Externí odkaz:
http://arxiv.org/abs/2307.09057
In this work we consider mean field type control problems with multiple species that have different dynamics. We formulate the discretized problem using a new type of entropy-regularized multimarginal optimal transport problems where the cost is a de
Externí odkaz:
http://arxiv.org/abs/2305.15292
Autor:
Ryner, Martin, Karlsson, Johan
In this paper we propose an adaptive approach for clustering and visualization of data by an orthogonalization process. Starting with the data points being represented by a Markov process using the diffusion map framework, the method adaptively incre
Externí odkaz:
http://arxiv.org/abs/2207.12279
In this work we develop a numerical method for solving a type of convex graph-structured tensor optimization problems. This type of problems, which can be seen as a generalization of multi-marginal optimal transport problems with graph-structured cos
Externí odkaz:
http://arxiv.org/abs/2112.05645
In this work, we consider the problem of bounding the values of a covariance function corresponding to a continuous-time stationary stochastic process or signal. Specifically, for two signals whose covariance functions agree on a finite discrete set
Externí odkaz:
http://arxiv.org/abs/2110.02728
Multi-marginal optimal transport (MOT) is a generalization of optimal transport to multiple marginals. Optimal transport has evolved into an important tool in many machine learning applications, and its multi-marginal extension opens up for addressin
Externí odkaz:
http://arxiv.org/abs/2110.00627