1 code implementation • 28 May 2023 • Ilgee Hong, Sen Na, Michael W. Mahoney, Mladen Kolar
Our method adaptively controls the accuracy of the randomized solver and the penalty parameters of the exact augmented Lagrangian, to ensure that the inexact Newton direction is a descent direction of the exact augmented Lagrangian.
1 code implementation • 29 Nov 2022 • Yuchen Fang, Sen Na, Michael W. Mahoney, Mladen Kolar
We propose a trust-region stochastic sequential quadratic programming algorithm (TR-StoSQP) to solve nonlinear optimization problems with stochastic objectives and deterministic equality constraints.
1 code implementation • 27 May 2022 • Sen Na, Michael W. Mahoney
We analyze a plug-in limiting covariance matrix estimator, and demonstrate the performance of the method both on benchmark nonlinear problems in CUTEst test set and on linearly/nonlinearly constrained regression problems.
1 code implementation • 20 Apr 2022 • Sen Na, Michał Dereziński, Michael W. Mahoney
Remarkably, we show that there exists a universal weighted averaging scheme that transitions to local convergence at an optimal stage, and still exhibits a superlinear convergence rate nearly (up to a logarithmic factor) matching that of uniform Hessian averaging.
no code implementations • NeurIPS 2021 • Sen Na
We study a real-time iteration (RTI) scheme for solving online optimization problem appeared in nonlinear optimal control.
1 code implementation • 23 Sep 2021 • Sen Na, Mihai Anitescu, Mladen Kolar
We study nonlinear optimization problems with a stochastic objective and deterministic equality and inequality constraints, which emerge in numerous applications including finance, manufacturing, power systems and, recently, deep neural networks.
1 code implementation • 10 Feb 2021 • Sen Na, Mihai Anitescu, Mladen Kolar
Based on the simplified deterministic algorithm, we then propose a non-adaptive SQP for dealing with stochastic objective, where the gradient and Hessian are replaced by stochastic estimates but the stepsizes are deterministic and prespecified.
1 code implementation • 3 Jul 2020 • Mingyuan Ma, Sen Na, Hongyu Wang
In extensive experiments on citation networks and other heterogeneous graphs, we demonstrate that adding autoencoder constraints significantly improves the performance of graph convolutional networks.
no code implementations • 14 May 2020 • Sen Na, Sungho Shin, Mihai Anitescu, Victor M. Zavala
We study the convergence properties of an overlapping Schwarz decomposition algorithm for solving nonlinear optimal control problems (OCPs).
no code implementations • ICML 2020 • Sen Na, Yuwei Luo, Zhuoran Yang, Zhaoran Wang, Mladen Kolar
We consider the bipartite graph and formalize its representation learning problem as a statistical estimation problem of parameters in a semiparametric exponential family distribution.
1 code implementation • 12 Sep 2019 • Sen Na, Mladen Kolar, Oluwasanmi Koyejo
Differential graphical models are designed to represent the difference between the conditional dependence structures of two groups, thus are of particular interest for scientific investigation.
1 code implementation • 30 Nov 2018 • Mingyuan Ma, Sen Na, Hongyu Wang, Congzhou Chen, Jin Xu
First, we build an interaction behavior graph for multi-level and multi-category data.
no code implementations • 27 Nov 2018 • Sen Na, Mladen Kolar
We study the estimation of the parametric components of single and multiple index volatility models.
1 code implementation • 16 Oct 2018 • Sen Na, Zhuoran Yang, Zhaoran Wang, Mladen Kolar
We study the parameter estimation problem for a varying index coefficient model in high dimensions.
no code implementations • 14 Nov 2017 • Sen Na, Mingyuan Ma, Mladen Kolar
Along with developing of Peaceman-Rachford Splittling Method (PRSM), many batch algorithms based on it have been studied very deeply.