论文标题
基于量化的优化：全局优化的替代随机近似
Quantization-Based Optimization: Alternative Stochastic Approximation of Global Optimization
论文作者
论文摘要
在这项研究中，我们提出了一种基于量化NP硬性问题目标函数的能量水平的全局优化算法。根据白噪声假设的量化误差，具有致密和均匀的分布，我们可以将量化误差视为I.I.D.白噪声。从随机分析中，仅在满足Lipschitz连续性的条件下而不是局部收敛属性（例如目标函数的Hessian限制），而不是在满足Lipschitz连续性的条件下会弱收敛。这表明所提出的算法可确保通过Laplace的条件进行全局优化。数值实验表明，所提出的算法在解决NP-HARD优化问题（例如旅行推销员问题）方面优于常规学习方法。
In this study, we propose a global optimization algorithm based on quantizing the energy level of an objective function in an NP-hard problem. According to the white noise hypothesis for a quantization error with a dense and uniform distribution, we can regard the quantization error as i.i.d. white noise. From stochastic analysis, the proposed algorithm converges weakly only under conditions satisfying Lipschitz continuity, instead of local convergence properties such as the Hessian constraint of the objective function. This shows that the proposed algorithm ensures global optimization by Laplace's condition. Numerical experiments show that the proposed algorithm outperforms conventional learning methods in solving NP-hard optimization problems such as the traveling salesman problem.
