stat.ML

Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems
We propose a trust-region stochastic sequential quadratic programming algorithm (TR-StoSQP) to solve nonlinear optimization problems …
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Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching
We consider solving equality-constrained nonlinear, nonconvex optimization problems. This class of problems appears widely in a variety …
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Inequality Constrained Stochastic Nonlinear Optimization via Active-Set Sequential Quadratic Programming
We study nonlinear optimization problems with a stochastic objective and deterministic equality and inequality constraints, which …
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A Theoretically Sound Sequential Quadratic Programming Algorithm On Riemannian Manifolds
We design a Sequential Quadratic Programming (SQP) algorithm for solving equality-constrained optimization problems on Riemannian …
Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence
We consider minimizing a smooth and strongly convex objective function using a stochastic Newton method. At each iteration, the …
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An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangians
We consider solving nonlinear optimization problems with a stochastic objective and deterministic equality constraints. We assume for …
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The Graph-Based Behavior-Aware Recommendation for Interactive News
Interactive news recommendation has been launched and attracted much attention recently. In this scenario, user’s behavior evolves from …
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High-dimensional Index Volatility Models via Stein's Identity
We study the estimation of the parametric components of single and multiple index volatility models. Using the first- and second-order …
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AEGCN: An Autoencoder-Constrained Graph Convolutional Network
We propose a novel neural network architecture, called autoencoder-constrained graph convolutional network, to solve node …
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Estimating Differential Latent Variable Graphical Models with Applications to Brain Connectivity
Differential graphical models are designed to represent the difference between the conditional dependence structures of two groups, and …
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