Physarum Powered Differentiable Linear Programming Layers and Applications.

ABSTRACT: Consider a learning algorithm, which involves an internal call to an optimization routine such as a generalized eigenvalue problem, a cone programming problem or even sorting. Integrating such a method as a layer(s) within a trainable deep neural network (DNN) in an efficient and numerically stable way is not straightforward - for instance, only recently, strategies have emerged for eigendecomposition and differentiable sorting. We propose an efficient and differentiable solver for general linear programming problems which can be used in a plug and play manner within DNNs as a layer. Our development is inspired by a fascinating but not widely used link between dynamics of slime mold (physarum) and optimization schemes such as steepest descent. We describe our development and show the use o

SUBMITTER: Meng Z 

PROVIDER: S-EPMC8415120 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature