An Efficient Algorithm for Matrix-Valued and Vector-Valued Optimal Mass Transport

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

Abstract

We present an efficient algorithm for recent generalizations of optimal mass transport theory to matrix-valued and vector-valued densities. These generalizations lead to several applications including diffusion tensor imaging, color image processing, and multi-modality imaging. The algorithm is based on sequential quadratic programming. By approximating the Hessian of the cost and solving each iteration in an inexact manner, we are able to solve each iteration with relatively low cost while still maintaining a fast convergence rate. The core of the algorithm is solving a weighted Poisson equation, where different efficient preconditioners may be employed. We utilize incomplete Cholesky factorization, which yields an efficient and straightforward solver for our problem. Several illustrative examples are presented for both the matrix and vector-valued cases.

Detaljer

Författare
  • Yongxin Chen
  • Eldad Haber
  • Kaoru Yamamoto
  • Tryphon T. Georgiou
  • Allen Tannenbaum
Enheter & grupper
Externa organisationer
  • Iowa State University
  • University of British Columbia
  • University of California, Irvine
  • Stony Brook University
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Beräkningsmatematik

Nyckelord

Originalspråkengelska
Sidor (från-till)79-100
TidskriftJournal of Scientific Computing
Volym77
Utgåva nummer1
Tidigt onlinedatum2018 mar 16
StatusPublished - 2018
PublikationskategoriForskning
Peer review utfördJa