Physarum Dynamics and Optimal Transport for Basis Pursuit

We study the connections between Physarum Dynamics and Dynamic Monge Kantorovich (DMK) Optimal Transport algorithms for the solution of Basis Pursuit problems. We show the equivalence between these two models and unveil their dynamic character by showing existence and uniqueness of the solution for all times and constructing a Lyapunov functional with negative Lie-derivative that drives the large-time convergence. We propose a discretization of the equation by means of a combination of implicit time-stepping and Newton method yielding an efficient and robust method for the solution of general basis pursuit problems. Finally, we propose a simple modification to the dynamic equation that can be interpreted as a gradient flow system for the Lyapunov functional. Several numerical experiments run on literature benchmark problems are used to show the accuracy, efficiency, and robustness of the proposed method.

[1]  D. Jordan,et al.  Nonlinear Ordinary Differential Equations: An Introduction for Scientists and Engineers , 1979 .

[2]  Jacek Gondzio,et al.  A Preconditioner for A Primal-Dual Newton Conjugate Gradient Method for Compressed Sensing Problems , 2014, SIAM J. Sci. Comput..

[3]  Pierre Seppecher,et al.  Mathématiques/Mathematics Shape optimization solutions via Monge-Kantorovich equation , 1997 .

[4]  Sara Daneri,et al.  Numerical Solution of Monge–Kantorovich Equations via a Dynamic Formulation , 2017, Journal of Scientific Computing.

[5]  A. Tero,et al.  Rules for Biologically Inspired Adaptive Network Design , 2010, Science.

[6]  Philip Hartman,et al.  Ordinary differential equations, Second Edition , 2002, Classics in applied mathematics.

[7]  Kurt Mehlhorn,et al.  Physarum can compute shortest paths , 2011, SODA.

[8]  Carl Tim Kelley,et al.  Numerical methods for nonlinear equations , 2018, Acta Numerica.

[9]  L. Evans,et al.  Differential equations methods for the Monge-Kantorovich mass transfer problem , 1999 .

[10]  T. Nakagaki,et al.  Intelligence: Maze-solving by an amoeboid organism , 2000, Nature.

[11]  Enrico Facca,et al.  Towards a Stationary Monge-Kantorovich Dynamics: The Physarum Polycephalum Experience , 2016, SIAM J. Appl. Math..

[12]  Amir Beck,et al.  On the Convergence of Alternating Minimization for Convex Programming with Applications to Iteratively Reweighted Least Squares and Decomposition Schemes , 2015, SIAM J. Optim..

[13]  R. Dembo,et al.  INEXACT NEWTON METHODS , 1982 .

[14]  Nisheeth K. Vishnoi,et al.  IRLS and Slime Mold: Equivalence and Convergence , 2016, ArXiv.

[15]  C. Villani Optimal Transport: Old and New , 2008 .

[16]  I. Ekeland,et al.  Convex analysis and variational problems , 1976 .

[17]  Allen Y. Yang,et al.  Fast ℓ1-minimization algorithms and an application in robust face recognition: A review , 2010, 2010 IEEE International Conference on Image Processing.

[18]  A. Tero,et al.  A mathematical model for adaptive transport network in path finding by true slime mold. , 2007, Journal of theoretical biology.

[19]  Jacek Gondzio,et al.  Matrix-free interior point method for compressed sensing problems , 2012, Mathematical Programming Computation.

[20]  Luca Bergamaschi,et al.  Spectral preconditioners for the efficient numerical solution of a continuous branched transport model , 2019, J. Comput. Appl. Math..