论文标题
混合整数非线性编程对矩阵的有损压缩
Lossy compression of matrices by black-box optimisation of mixed integer nonlinear programming
论文作者
论文摘要
在边缘计算中，抑制数据大小是执行复杂任务（例如自动驾驶）的机器学习模型的挑战，其中计算资源（速度，内存大小和功率）受到限制。通过将其分解为整数和真实矩阵的乘积，已经引入了矩阵数据的有效损耗压缩。但是，它的优化很困难，因为它需要同时优化整数和真实变量。在本文中，我们通过利用最近开发的黑盒优化（BBO）算法来改善这种优化，并具有用于整数变量的ISING求解器。此外，该算法可用于解决分别在真实和整数变量方面线性和非线性的混合组件编程问题。讨论了ISINS求解器的选择（模拟退火，量子退火和模拟淬火）与BBO算法（BOCS，FMQA及其变化）的策略之间的差异，以进一步开发BBO技术。
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.
