Isotonicity of the proximity operator and stochastic optimization problems in Hilbert quasi-lattices endowed with Lorentz cones

Dezhou Kong, Li Sun, Haibin Chen, Yun Wang
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Abstract

In this paper, we discuss the isotonicity of the proximity operator in Hilbert quasi-lattices endowed with different Lorentz cones. The extended Lorentz cone is first defined by the Minkowski functionals of some subsets. We then establish some sufficient conditions for the isotonicity of the proximity operator concerning one order and two mutually dual orders induced by Lorentz cones, respectively. Similarly, the cases of the extended Lorentz cones and other ordered inequality properties of the proximity operator are analysed. By adopting these characterizations, some solvability and iterative algorithm theorems for the stochastic optimization problem are established by different order approaches. For solvability, the gradient of the mappings does not need to be continuous, and the solutions are optimal with respect to the orders. In the stochastic proximal algorithms, the mappings satisfy inequality conditions just for comparable elements, but the convergence direction and convergence rate are more optimal.
具有Lorentz锥的Hilbert拟格中邻近算子的等压性及随机优化问题
本文讨论了具有不同洛伦兹锥的Hilbert拟格中邻近算子的等压性。扩展洛伦兹锥首先由若干子集的闵可夫斯基泛函定义。然后分别建立了由洛伦兹锥诱导的一阶和两互对偶阶邻近算子等压性的充分条件。同样地,分析了邻近算子的扩展洛伦兹锥和其他有序不等式性质的情况。利用这些表征,用不同阶次的方法建立了随机优化问题的可解性定理和迭代算法定理。对于可解性,映射的梯度不需要连续,且解相对于阶数是最优的。在随机近邻算法中,映射只对可比较元素满足不等式条件,但收敛方向和收敛速度更优。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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