  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 integer
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 differences between the choice of Ising
solvers (simulated annealing, quantum annealing and simulated quenching) and
the strategies of the BBO algorithms (BOCS, FMQA and their variations) are
discussed for further development of the BBO techniques.
