论文标题
用于无约束优化的神经信任区域算法（第1部分）
Neural-trust-region algorithm for unconstrained optimization (Part 1)
论文作者
论文摘要
在本文（第1部分）中，我们描述了一种无衍生的信任区域方法，用于解决无约束的优化问题。当我们放松模型顺序假设并使用人工神经网络技术来构建计算上相对便宜的模型时，我们将讨论一种方法。我们直接找到了目标函数最小化器的估计，而无需明确构建模型函数。因此，我们需要具有神经网络模型衍生物，这可以通过后传播过程获得。
In this paper (part 1), we describe a derivative-free trust-region method for solving unconstrained optimization problems. We will discuss a method when we relax the model order assumption and use artificial neural network techniques to build a computationally relatively inexpensive model. We directly find an estimate of the objective function minimizer without explicitly constructing a model function. Therefore, we need to have the neural-network model derivatives, which can be obtained simply through a back-propagation process.
