I moved to Argonne National Laboratory in August of 2018. I work with Mihai Anitescu at the MCS division. I was a postdoctoral researcher at the Wisconsin Institute for Discovery, working with Prof. Michael Ferris. I'm broadly interested in the area of linear/nonlinear optimization and complementarity problems with an emphasis on equilibrium programming and variational inequalities.
I received my Ph.D. degree in Computer Sciences from the University of Wisconsin-Madison under the supervision of Prof. Michael Ferris. I received M.S. in Computer Science and Engineering and B.S. in Computer Science and Engineering and Mathematics, both from POSTECH in South Korea.
Youngdae Kim, Michael C. Ferris: Solving equilibrium problems using extended mathematical programming. Mathematical Programming Computation. 11(3): 457-501, 2019 (project page)
Youngdae Kim, Olivier Huber, Michael C. Ferris: A structure-preserving pivotal method for affine variational inequalities. Mathematical Programming, Series B. 168(1-2): 93-121, 2018
Noam Goldberg, Youngdae Kim, Sven Leyffer, Thomas D. Veselka: Adaptively refined dynamic program for linear spline regression. Computational Optimization and Applications. 58(3): 523-541 (2014)
Youngdae Kim, Gae-won You, Seung-won Hwang: Ranking strategies and threats: a cost-based pareto optimization approach. Distributed and Parallel Databases. 26(1): 127-150 (2009)
Hwanjo Yu, Ilhwan Ko, Youngdae Kim, Seung-won Hwang, Wook-shin Han: Exact indexing for support vector machines. In Proc. of the ACM SIGMOD Conference on Data Management, June 12-16, 2011, Athens, Greece, pp. 709-720
Youngdae Kim, Sven Leyffer, Todd Munson: MPEC methods for bilivel optimization problems
Wonjun Chang, Michael C. Ferris, Youngdae Kim, Thomas F. Rutherford: Solving stochastic dynamic programming problems: a mixed complementarity approach.
Work in progress
Youngdae Kim, Michael C. Ferris: SELKIE: a model transformation and distributed solver for structured equilibrium problems.
Youngdae Kim, Michael C. Ferris: An efficient model generation for decomposition methods in modeling languages.
SELKIE is an agent-based decomposition method for equilibrium problems. It exploits agents' structure to decompose a given model into smaller sub-models, possibly amenable to parallel computations, so that we can find a more robust and faster solution path.
SELKIE will be available in GAMS (General Algebraic Modeling System) 26.0.0 so that users do not have to create interfaces tailored to it.
PATH VI is a Newton-based complementary pivotal method for variational inequalities. It can be used to solve some equilibrium problems such as generalized Nash equilibrium problems (GNEP) and multiple optimization problems with equilibrium constraints (MOPEC) in one-shot.
PATH VI will be integrated into GAMS (General Algebraic Modeling System) so that users do not have to create interfaces tailored to PATH VI.
It defines a new set of constructs that enable a natural translation of the algebraic formulation of equilibrium problems into modeling languages such as GAMS. It automatically reformulates the given equilibrium problem into a corresponding mixed complementarity problem (MCP). It also provides constructs to exploit the problem structure by the back-end solvers.
The new JAMS is available within GAMS with version 25.1.0 beta or higher.
Block LU update routine provides an efficient rank-1 update, especially for large-scale problems. It can be used with existing basis factorization routines such as LUSOL and UMFPACK, and shows significant performance improvement on large-scale equilibrium problems. It is being currently used as one of the linear algebra engines for PATH and PATH VI solvers.