Lossy compression of matrices by black box optimisation of mixed integer nonlinear programming.

ABSTRACT: In edge computing, suppressing data size is a challenge for machine learning models that perform complex tasks such as autonomous driving, in which computational resources (speed, memory size and power) are limited. Efficient lossy compression of matrix data has been introduced by decomposing it into the product of an integer and real matrices. However, its optimisation is difficult as it requires simultaneous optimisation of an integer and real variables. In this paper, we improve this optimisation by utilising recently developed black-box optimisation (BBO) algorithms with an Ising solver for binary variables. In addition, the algorithm can be used to solve mixed-integer programming problems that are linear and non-linear in terms of real and integer variables, respectively. The differen

SUBMITTER: Kadowaki T 

PROVIDER: S-EPMC9477857 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature