Learning for Integer-Constrained Optimization through Neural Networks with Limited Training
Learning for Integer-Constrained Optimization through Neural Networks with Limited Training
复制标题
通过有限训练的神经网络学习整数约束优化
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Lingjia Liu
中科院分区：
文献类型：
--
作者：
Zhou Zhou;Shashank Jere;Lizhong Zheng;Lingjia Liu
在本文中，我们研究了一种基于神经网络的学习方法，该方法使用非常有限的训练来解决整数约束规划问题。具体地说，我们引入了一个对称的和分解的神经网络结构，它是完全可解释的功能方面的组成部分。通过利用整数约束的潜在模式，以及目标函数的仿射性质，与其他不利用整数约束固有结构的通用神经网络结构相比，引入的神经网络在有限的训练下提供了优越的泛化性能。此外，我们还证明了所引入的分解方法可以进一步扩展到半分解框架。在可用的训练集通常有限的无线通信系统背景下，通过分类/符号检测任务来评估所引入的学习方法。评估结果表明，所引入的学习策略能够在3GPP社区规定的各种无线信道环境中有效地执行分类/符号检测任务。
In this paper, we investigate a neural network-based learning approach towards solving an integer-constrained programming problem using very limited training. To be specific, we introduce a symmetric and decomposed neural network structure, which is fully interpretable in terms of the functionality of its constituent components. By taking advantage of the underlying pattern of the integer constraint, as well as of the affine nature of the objective function, the introduced neural network offers superior generalization performance with limited training, as compared to other generic neural network structures that do not exploit the inherent structure of the integer constraint. In addition, we show that the introduced decomposed approach can be further extended to semi-decomposed frameworks. The introduced learning approach is evaluated via the classification/symbol detection task in the context of wireless communication systems where available training sets are usually limited. Evaluation results demonstrate that the introduced learning strategy is able to effectively perform the classification/symbol detection task in a wide variety of wireless channel environments specified by the 3GPP community.
影响因子：
10.4
作者：
Khani, Mehrdad;Alizadeh, Mohammad;Fleming, Phil
通讯作者：
Fleming, Phil