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

Yongxin Chen, Eldad Haber, Kaoru Yamamoto, Tryphon T. Georgiou, Allen Tannenbaum

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)79-100
Number of pages22
JournalJournal of Scientific Computing
Issue number1
Publication statusPublished - Oct 1 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • General Engineering
  • Computational Mathematics
  • Theoretical Computer Science
  • Applied Mathematics
  • Numerical Analysis
  • Computational Theory and Mathematics


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