# Deep Policy Iteration with Integer Programming for Inventory Management

Finally, to make RL algorithms more accessible for inventory management researchers, we also discuss a modular Python library developed that can be used to test the performance of RL algorithms with various supply chain structures.

# A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees

Several recent publications report advances in training optimal decision trees (ODT) using mixed-integer programs (MIP), due to algorithmic advances in integer programming and a growing interest in addressing the inherent suboptimality of heuristic approaches such as CART.

# Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network

Unlike the classical variational inference, where a factorized distribution is used to approximate the posterior, we employ a feedforward neural network supplemented by an encoder recurrent neural network to develop a more flexible probabilistic model.

# Finite-Sum Smooth Optimization with SARAH

The total complexity (measured as the total number of gradient computations) of a stochastic first-order optimization algorithm that finds a first-order stationary point of a finite-sum smooth nonconvex objective function $F(w)=\frac{1}{n} \sum_{i=1}^n f_i(w)$ has been proven to be at least $\Omega(\sqrt{n}/\epsilon)$ for $n \leq \mathcal{O}(\epsilon^{-2})$ where $\epsilon$ denotes the attained accuracy $\mathbb{E}[ \|\nabla F(\tilde{w})\|^2] \leq \epsilon$ for the outputted approximation $\tilde{w}$ (Fang et al., 2018).

# DTN: A Learning Rate Scheme with Convergence Rate of $\mathcal{O}(1/t)$ for SGD

This paper has some inconsistent results, i. e., we made some failed claims because we did some mistakes for using the test criterion for a series.

# When Does Stochastic Gradient Algorithm Work Well?

In this paper, we consider a general stochastic optimization problem which is often at the core of supervised learning, such as deep learning and linear classification.

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