D
Dinakar Gade
Researcher at Ohio State University
Publications - 12
Citations - 415
Dinakar Gade is an academic researcher from Ohio State University. The author has contributed to research in topics: Solver & Relaxation (approximation). The author has an hindex of 7, co-authored 12 publications receiving 351 citations. Previous affiliations of Dinakar Gade include Sabre Corporation & Iowa State University.
Papers
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Journal ArticleDOI
Obtaining lower bounds from the progressive hedging algorithm for stochastic mixed-integer programs
Dinakar Gade,Gabriel A. Hackebeil,Sarah M. Ryan,Jean-Paul Watson,Roger J.-B. Wets,David L. Woodruff +5 more
TL;DR: This work presents a method for computing lower bounds in the progressive hedging algorithm (PHA) for two-stage and multi-stage stochastic mixed-integer programs, and explores the relationship between key PHA parameters and the quality of the resulting lower bounds.
Journal ArticleDOI
Decomposition algorithms with parametric Gomory cuts for two-stage stochastic integer programs
TL;DR: The proposed decomposition algorithms akin to the $$L$$-shaped or Benders’ methods are developed by utilizing Gomory cuts to obtain iteratively tighter approximations of the second-stage integer programs.
Journal ArticleDOI
Toward scalable stochastic unit commitment. Part 2: Solver Configuration and Performance Assessment
Kwok W. Cheung,Dinakar Gade,Cesar A. Silva-Monroy,Sarah M. Ryan,Jean-Paul Watson,Roger J.-B. Wets,David L. Woodruff +6 more
TL;DR: This work describes critical, novel customizations of the progressive hedging algorithm for SUC, a scenario-based decomposition strategy for solving stochastic programs in tractable run-times, and establishes a rigorous baseline for both solution quality and run times of SUC solvers.
Journal ArticleDOI
Sample average approximation applied to the capacitated-facilities location problem with unreliable facilities
Dinakar Gade,Edward A. Pohl +1 more
TL;DR: In this paper, a stochastic programming formulation for the capacitated-facilities location problem with unreliable facilities is presented and the benefit of investing in redundant facility locations is demonstrated.
Proceedings ArticleDOI
A new approximation method for generating day-ahead load scenarios
TL;DR: A stochastic model for hourly load on a given day, within a segment of similar days, based on a weather forecast available on the previous day is developed, which approximates trends and error distributions for the load forecasts by optimizing within a new class of functions specified by a finite number of parameters.