22 citations · 34 across the 6 of their papers we have counts for
7 papers
Bounding-Focused Discretization Methods for the Global Optimization of Nonconvex Semi-Infinite Programs
Evren M. Turan, Johannes Jäschke, Rohit Kannan
We use sensitivity analysis to design bounding-focused discretization (cutting-surface) methods for the global optimization of nonconvex semi-infinite programs (SIPs). We begin by…
Strong Partitioning and a Machine Learning Approximation for Accelerating the Global Optimization of Nonconvex QCQPs
Rohit Kannan, Harsha Nagarajan, Deepjyoti Deka
We learn optimal instance-specific heuristics for the global minimization of nonconvex quadratically-constrained quadratic programs (QCQPs). Specifically, we consider partitioning-…
Data-Driven Sample Average Approximation with Covariate Information
Rohit Kannan, Güzin Bayraksan, James R. Luedtke
We study optimization for data-driven decision-making when we have observations of the uncertain parameters within the optimization model together with concurrent observations of c…
Heteroscedasticity-aware residuals-based contextual stochastic optimization
Rohit Kannan, Güzin Bayraksan, James Luedtke
We explore generalizations of some integrated learning and optimization frameworks for data-driven contextual stochastic optimization that can adapt to heteroscedasticity. We ident…
Residuals-based distributionally robust optimization with covariate information
Rohit Kannan, Güzin Bayraksan, James R. Luedtke
We consider data-driven approaches that integrate a machine learning prediction model within distributionally robust optimization (DRO) given limited joint observations of uncertai…
Stochastic DC Optimal Power Flow With Reserve Saturation
Rohit Kannan, James R. Luedtke, Line A. Roald
We propose an optimization framework for stochastic optimal power flow with uncertain loads and renewable generator capacity. Our model follows previous work in assuming that gener…
A stochastic approximation method for approximating the efficient frontier of chance-constrained nonlinear programs
Rohit Kannan, James Luedtke
We propose a stochastic approximation method for approximating the efficient frontier of chance-constrained nonlinear programs. Our approach is based on a bi-objective viewpoint of…