3 citations · 7 across the 7 of their papers we have counts for
8 papers
Weakly Time-Coupled Approximation of Markov Decision Processes
Negar Soheili, Selvaprabu Nadarajah, Bo Yang
Finite-horizon Markov decision processes (MDPs) with high-dimensional exogenous uncertainty and endogenous states arise in operations and finance, including the valuation and exerc…
Offline-Online Reinforcement Learning for Energy Pricing in Office Demand Response: Lowering Energy and Data Costs
Doseok Jang, Lucas Spangher, Manan Khattar +3
Our team is proposing to run a full-scale energy demand response experiment in an office building. Although this is an exciting endeavor which will provide value to the community,…
Adaptive Risk Mitigation in Demand Learning
Parshan Pakiman, Boxiao Chen, Selvaprabu Nadarajah +1
We study dynamic pricing of a product with an unknown demand distribution over a finite horizon. Departing from the standard no-regret learning environment in which prices can be a…
An Adaptive Parameter-free and Projection-free Restarting Level Set Method for Constrained Convex Optimization Under the Error Bound Condition
Qihang Lin, Negar Soheili, Runchao Ma +1
Recent efforts to accelerate first-order methods have focused on convex optimization problems that satisfy a geometric property known as error-bound condition, which covers a broad…
Self-guided Approximate Linear Programs
Parshan Pakiman, Selvaprabu Nadarajah, Negar Soheili +1
Approximate linear programs (ALPs) are well-known models based on value function approximations (VFAs) to obtain policies and lower bounds on the optimal policy cost of discounted-…
Pathwise Optimization for Merchant Energy Production
Bo Yang, Selvaprabu Nadarajah, Nicola Secomandi
We study merchant energy production modeled as a compound switching and timing option. The resulting Markov decision process is intractable. State-of-the-art approximate dynamic pr…
A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints
Qihang Lin, Selvaprabu Nadarajah, Negar Soheili +1
Stochastic convex optimization problems with expectation constraints (SOECs) are encountered in statistics and machine learning, business, and engineering. In data-rich environment…
Partial hyperplane activation for generalized intersection cuts
Aleksandr M. Kazachkov, Selvaprabu Nadarajah, Egon Balas +1
The generalized intersection cut (GIC) paradigm is a recent framework for generating cutting planes in mixed integer programming with attractive theoretical properties. We investig…