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20152023
most citedLeveraging GPU batching for scalable nonlinear programming through massive Lagrangian decomposition

14 citations · 50 across the 18 of their papers we have counts for

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12 papers · 1 filter

math.OC2023

Differentially Private Distributed Convex Optimization

Minseok Ryu, Kibaek Kim

This paper considers distributed optimization (DO) where multiple agents cooperate to minimize a global objective function, expressed as a sum of local objectives, subject to some…

math.OC2023

Polytopic Superset Algorithm for Nonlinear Robust Optimization

Bowen Li, Kibaek Kim, Sven Leyffer

Nonlinear robust optimization (NRO) is widely used in different applications, including energy, control, and economics, to make robust decisions under uncertainty. One of the class…

math.OC2022★ 3 cited

Linearized Optimal Power Flow for Multiphase Radial Networks with Delta Connections

Geunyeong Byeon, Minseok Ryu, Kibaek Kim

This paper proposes a linear approximation of the alternating current optimal power flow problem for multiphase distribution networks with voltage-dependent loads connected in both…

math.OC2022★ 3 cited

Two-Stage Distributionally Robust Conic Linear Programming over 1-Wasserstein Balls

Geunyeong Byeon, Kaiwen Fang, Kibaek Kim

This paper studies two-stage distributionally robust conic linear programming under constraint uncertainty over type-1 Wasserstein balls. We present optimality conditions for the d…

math.OC2021

A mixed complementarity problem approach for steady-state voltage and frequency stability analysis

Youngdae Kim, Kibaek Kim

We present a mixed complementarity problem (MCP) approach for a steady-state stability analysis of voltage and frequency of electrical grids. We perform a theoretical analysis prov…

math.OC2021★ 1 cited

Accelerated Computation and Tracking of AC Optimal Power Flow Solutions using GPUs

Youngdae Kim, Kibaek Kim

We present a scalable solution method based on an alternating direction method of multipliers and graphics processing units (GPUs) for rapidly computing and tracking a solution of…

math.OC2021

Numerical Performance of Different Formulations for Alternating Current Optimal Power Flow

Sayed Abdullah Sadat, Kibaek Kim

Alternating current optimal power flow (ACOPF) problems are nonconvex and nonlinear optimization problems. Utilities and independent service operators (ISO) require ACOPF to be sol…

math.OC2021★ 14 cited

Leveraging GPU batching for scalable nonlinear programming through massive Lagrangian decomposition

Youngdae Kim, François Pacaud, Kibaek Kim +1

We present the implementation of a trust-region Newton algorithm ExaTron for bound-constrained nonlinear programming problems, fully running on multiple GPUs. Without data transfer…

math.OC2021

On the Tightness of the Lagrangian Dual Bound for Alternating Current Optimal Power Flow

Weiqi Zhang, Kibaek Kim, Victor M. Zavala

We study tightness properties of a Lagrangian dual (LD) bound for the nonconvex alternating current optimal power flow (ACOPF) problem. We show an LD bound that can be computed in…

math.OC2021★ 5 cited

A Privacy-Preserving Distributed Control of Optimal Power Flow

Minseok Ryu, Kibaek Kim

We consider a distributed optimal power flow formulated as an optimization problem that maximizes a nondifferentiable concave function. Solving such a problem by the existing distr…

math.OC2020

Data-driven two-stage conic optimization with zero-one uncertainties

Anirudh Subramanyam, Mohamed El Tonbari, Kibaek Kim

We address high-dimensional zero-one random parameters in two-stage convex conic optimization problems. Such parameters typically represent failures of network elements and constit…

math.OC2016★ 6 cited

Data Centers as Dispatchable Loads to Harness Stranded Power

Kibaek Kim, Fan Yang, Victor M. Zavala +1

We analyze how both traditional data center integration and dispatchable load integration affect power grid efficiency. We use detailed network models, parallel optimization solver…