整数约束优化问题的有限训练神经网络学习
Learning for Integer-Constrained Optimization through Neural Networks with Limited Training
摘要：
在本文中，我们研究了一个基于神经网络的学习方法解决整数约束规划问题，使用非常有限的训练。具体来说，我们引入了一个对称和分解的神经网络结构，这是完全可以解释的功能，其组成部分。通过利用整数约束的基本模式以及目标函数的仿射性质，与不利用整数约束的固有结构的其他通用神经网络结构相比，所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，我们表明，所介绍的分解方法可以进一步扩展到半分解框架。所介绍的学习方法进行评估，通过分类/符号检测任务的背景下，可用的训练集通常是有限的无线通信系统。评估结果表明，引入的学习策略是能够有效地执行分类/符号检测任务，在各种各样的无线信道环境中指定的3GPP社区。
英文摘要：
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.
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