S.Kim

Sunyoung Kim Department of Mathematics Ewha W. University
Home	Vita	Teaching	Research	Links

S. Kim and M. Kojima, Generating cutting inequlities successively for binary quadratic optimization problems, arXiv:2107.08665, July 2021.

G. Azuma, M. Fukuda, S. Kim, and M. Yamashita, Exact SDP relaxation of quadratically constrained quadratic programs with forest structures, arXiv:2009.02638, Sept. 2020.

S. Kim, M. Kojima and M. Yamashita, Parallel Implementation of Successive Sparse SDP Relaxation for Large-scale Euclidean Distance Geometry Problems Research Report B-470, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, Nov. (2012).

K. Fujisawa, S. Kim, M. Kojima, Y. Okamoto, and M. Yamashita, User manual for SparseCoLO: Conversion Methods for SPARSE Conic-form Linear Optimization Problems Research Report B-453, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, Feb. (2009). A Matlab package SparseCoLO.

S. Kim, M. Kojima, and K. C. Toh, A Newton-bracketing method for a simple conic optimization problem", arXiv:1905.12840, May 2019. Optimization Methods and Software, 36(2-3) 371-388 (2021).

S. Kim, M. Kojima, and K. C. Toh, Doubly Nonnegative Relaxation for Quadratic and Polynomial Optimization Problems with Binary and Box Constraints Research Report B-483, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, July (2016), to appear in Mathematical Programming.

S. Kim, M. Kojima, and K. C. Toh, A Geometric Analysis of a Class of Nonconvex Conic Programs for Convex Conic Reformulations of Quadratic and Polynomial Optimization Problems, arXiv:1901.02179, Jan. (2019), SIAM J. on Optimization, 30(2) 1251-1273 (2020).

S. Kim, M. Kojima, and K. C. Toh, Doubly nonnegative relaxations are equivalent to completely positive reformulations of quadratic optimization problems with block-clique graph structures", arXiv:1903.07325, March 2019. arXiv:1903.07325, March (2019), Journal of Global Optimization 77(3) 513-541 (2020).

T. Nakagaki, M. Fukuda, S. Kim, and M. Yamashita A Dual Spectral Projected Gradient Method for Log-determinant Semidefinite Problems arXiv:1812.00523, Nov. (2018), Computational Optimization and Applications, 76(1) 33-68 (2020).

H. Komeiji, S. Kim and M. Yamashita, Sums of Squares Representation of Polynomials by Alternating Directional Augmented Lagrangian Methods with Fast Convergence Research Report B-488, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, March (2018), Computational Optimization and Applications, 74, 317-344 (2019).

M. Kimizuka, S. Kim and M. Yamashita, Solving Pooling Problems by SOCP and LP Relaxations and Rescheduling Methods Research Report B-487, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, Feb. (2018), Journal of Global Optimization, 75(3) 631-654 (2019).

N. Ito, S. Kim, M. Kojima, A. Takeda, and K. C. Toh, BBCPOP: a MATLAB Software for Polynomial Optimization Problems with Binary, Box and Complementarity Constraints arXiv:1804.00761 March (2018), ACM Transaction on Mathematical Software, 45(3) 34 (2019).

N. Ito, S. Kim, M. Kojima, A. Takeda, and K. C. Toh, Equivalences and Differences in Conic Relaxations of Combinatorial Optimization Problems Research Report B-486, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, July (2017) Journal of Global Optimization, 72(4) 619-653 (2018).

N. Arima, S. Kim, M. Kojima, and K. C. Toh, Lagrangian-Conic Relaxations, Part II: Applications to Polynomial Optimization Problems Research Report B-476, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, Jan. (2014), Pacific J. Optim, 15(3) 415-439 (2019).

S. Sakaue, A. Takeda, S. Kim, and N. Ito, Exact SDP relaxations with truncated moment matrix for binary polynomial optimization problems Technical Report METR 2016-01, Department of Mathematical Informatics, The University of Tokyo, Jan. (2016). SIAM J. on Optimization, 27(1) 565-582 (2017).
N. Arima, S. Kim, M. Kojima, and K. C. Toh, Lagrangian-Conic Relaxations, Part I: A Unified Framework and Its Applications to Quadratic Optimization Problems Research Report B-475, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, Jan. (2014), Pacific J. Optim. 14(1) 161-192 (2018).

N. Arima, S. Kim, M. Kojima, and K. C. Toh, A Robust Lagrangian-DNN Method for a Class of Quadratic Optimization Problems Research Report B-482, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, Feb. (2016). Computational Optimization and Applications, 66(3) 453-479 (2017).

S. Kim and M. Kojima, Binary Quadratic Optimization Problems That Are Difficult to Solve by Conic Relaxations Research Report B-481, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, July (2015). Discrete Optimization, 24, 170-183 (2017).

V. Jeyakumar, S. Kim, G.M. Lee, and G. Li, Solving Global Optimization Problems with Sparse Polynomials and Unbounded Semialgebraic Feasible Sets, Journal of Global Optimization, Vol 65, 175-190 (2016).

N. Arima, S. Kim and M. Kojima, Extension of Completely Positive Cone Relaxation to Polynomial Optimization Research Report B-471, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, Feb. (2013). Journal of Optimization Thoery and Applications, Vol. 168, 884-900 (2016).

S. Kim, M. Kojima and K. C. Toh, A Lagrangian-DNN Relaxation: a Fast Method for Computing Tight Lower Bounds for a Class of Quadratic Optimization Problems Research Report B-472, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, Oct. (2013). Mathematical Programming Vol 156, 161-187 (2016).

N. Arima, S. Kim and M. Kojima, Simplified Copositive and Lagrangian Relaxations for Linearly Constrained Quadratic Optimization Problems in Continuous and Binary Variables Research Report B-469, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, Oct. (2012), Pacific Journal of Optimization Vol 10, No 3, 437-451 (2014).

S. Burer, S. Kim and M. Kojima, Faster, but Weaker, Relaxations for Quadratically Constrained Quadratic Programs April (2013), Computational Optimization and Applications, Vol. 59, 27-45 (2014).

N. Arima, S. Kim and M. Kojima, A Quadratically Constrained Quadratic Optimization Model for Completely Positive Cone Programming Research Report B-468, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, Sept. (2012), SIAM J. on Optimization, Vol 23, No. 4, 2320- 2340 (2013).

S. Kim and M. Kojima, A Continuation Method for Large-sized Sensor Network Localization Problems Research Report B-466, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, August (2011) Pacific Journal of Optimization, Vol 9, No.1 117-136 (2013).

S. Kim, M. Kojima, H. Waki, and M. Yamashita, Algorithm 920: SFSDP: a Sparse Version of Full SemiDefinite Programming relaxation for sensor network localization problems Research Report B-457, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, July (2009). A Matlab package SFSDP, ACM Transaction on Mathematical Software, 38, 4, 2012.

S. Kim and M. Kojima, Exploiting Sparsity in SDP Relaxation of Polynomial Optimization Problems Research Report B-462, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, May (2010) Handbook on Semidefinite, Conic and Polynomial Optimization: theory, algorithm, software and applications, M. Anjos and J.B. Lasserre eds., Nov. (2011) 499-532.

S. Kim, M. Kojima, M. Mevissen and M. Yamashita, Exploiting Sparsity in Linear and Nonlinear Matrix Inequalities via Positive Semidefinte Matrix Completion Research Report B-452, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, Dec. (2009), Math. Programming, Vol. 129, No. 1, 33-68 (2011).

S. Kim and M. Kojima, Solving Polynomial Least Squares Problems via Semidefinite Programming Relaxations Research Report B-444, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, Aug. (2007), Journal of Global Optimization, Vol 46., No.1, 1-23 (2010).

S. Kim, M. Kojima, and H. Waki, Exploiting Sparsity in SDP Relaxation for Sensor Network Localization Research Report B-447, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, Jan. (2008), SIAM J. on Optimization, Vol. 20, No. 1, 192-215 (2009). Listed in Top 20 Most Downloaded Articles of SIAM Journal on Optimization --April 2009.

S. Kim, M. Kojima and Ph. L. Toint, Recognizing Underlying Sparsity in Optimization, Research Report B-428, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, May (2006), Mathematical Programming, Vol. 119, No. 2, 273-303 (2009).

K. Kobayashi, S. Kim and M. Kojima, Sparse Second Order Cone Programming Formulations for Convex Optimization Problems Research Report B-440, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, March (2007) Journal of Operations Research Society of Japan, Vol 51, No. 3, 241-264 (2008).

H. Waki, S. Kim, M. Kojima, M. Muramatsu, SparsePOP: a Sparse Semidefinite Programming Relaxation of Polynomial Optimization Problems, Research Report B-414, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, Revised Aug. (2007), ACM Transaction on Mathematical Software, Vol 35, 2, Algorithm 883, Article No. 15 (2008).

K. Kobayashi, S. Kim and M. Kojima, Correlative Sparsity in Primal-Dual Interior-Point Method for LP, SDP and SOCP Research Report B-434, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, September (2006), Applied Mathematics and Optimization, Vol 58, 69-88 (2008).

T. Gunji, S. Kim, K. Fujisawa, M. Kojima, PHoMpara - Parallel Implementation of the Polyhedral Homotopy Continuation Method, Research Report B-419, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, Oct. (2005), Computing, Vol. 77, No. 4, 387-411 (2006).

H. Waki, S. Kim, M. Kojima, M. Muramatsu, Sums of Squares and Semidefinite Programming Relaxations for Polynomial Optimization Problems with Structured Sparsity, Research Report B-411, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, Oct. (2004), SIAM Optimization, Vol 17, No. 1, 218-242 (2006). Listed in "Top 20 Most Downloaded Articles among SIOPT articles -- Semptember 2008 "

M. Kojima, S. Kim and H. Waki, Sparsity in Sums of Squares of Polynomials Research Report B-391, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, June (2003), Mathematical Programming, Vol 103, No.1 45-62 (2005).

S. Kim, M. Kojima and H. Waki, Generalized Lagrangian Duals and Sums of Squares Relaxations of Sparse Polynomial Optimization Problems Research Report B-395, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, SIAM Optimization, Vol 15, No. 3, 697-719 (2005).

S. Kim and M. Kojima, Numerical Stability of Path Tracing in Polyhderal Homotopy Continuation Methods Research Report B-390, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, March (2003), Computing, Vol 73, No. 4 329-348 (2004).

H. Waki, M. Kojima and S. Kim, Relaxation method for optimization problems with polynomial constraints over cones, Research Report B-399, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, Dec. (2003), Mathematics and Algorithms, RIMS Lecture Notes, Kyoto University (2003).

T. Gunji, S. Kim, M. Kojima, A. Takeda, K. Fujisawa and T. Mizutani, PHoM - a Polyhderal Homotopy Continuation Method for Polynomial Systems Research Report B-386, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, December (2002), Computing, Vol 73, No. 1 57-77, (2004).

S. Kim, M. Kojima and M. Yamashita Second Order Cone Programming Relaxation of a Positive Semidefinite Constraint Research Report B-381, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, (2002), Optimization Methods and Software Vol 18, No. 5, 535-541 (2003).

S. Kim and M. Kojima, Exact Solutions of Some Nonconvex Quadratic Optimization Problems via SDP and SOCP relaxations Research Report B-375, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology (2002), Computational Optimization and Applications, Vol. 26, No. 2, 143-154 (2003).

M. Kojima, S. Kim and H. Waki, A General Framework for Convex Relaxation of Polynomial Optimization Problems over Cones Journal of Operation Research Society of Japan, Vol. 46, No. 2, 125-144 (2003).

Y. Dai, S. Kim and M. Kojima, Computing All Nonsingular Solutions of Cyclic-n Polynomial Using Polyhedral Homotopy Continuation Methods, Research Report B-373, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology (2001), Solution Information of Some Polynomial Systems, J. of Computational and Applied Mathematics, Vol. 152, 1/2, 83-97 (2003).

S. Kim and M. Kojima, CMPSc: A Continuation Mathod for Polynomial Systems (C++ Version) , Research Report B-378, Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology (2002).

S. Kim and M. Kojima, CMPSm: A Continuation Mathod for Polynomial Systems (Matlab Version), Mathematical Software, ICMS2002 Beijing, China, August 17-19 (Editors: Arjeh M Cohen, Xiao-Shan Gao and Nobuki Takakayama), World Scientific, 248-259 (2002).
H. Ahn, H. Moon, S. Kim and R. Kodell, A Newton-Based Approach for Attributing Tumor Lethality in Animal Carcinogenicity Studies, Computational Statistics and Data Analysis, 38, No. 3, 263-283 (2002).
S. Kim, New Convergence Conditions of Secant Methods via Alpha Theory, J. KSIAM, 5, No. 2, 101-115 (2001).
S. Kim and M. Kojima, Second Order Cone Programming Relaxation Methods of Nonconvex Quadratic Optimization Problem, Optimization Methods and Software, 15, No. 3, 201-224 (2001).
S. Kim and M. Kojima, Second Order Cone Programming for Quadratic Optimization, Japan-Korean Workshop on Algorithms and Computation, 1, 1-8 (2000).
S. Kim, A Convergence Criterion of Secant Methods with Approximate Zeros, Kor. J. of Comp. & Appl. Math., 6, No.3, 575-588 (1999).
S. Kim, Numerical Solutions of CSIE, Comp. & Math. with Applic. 38, No,5,183-195 (1999).
S. Kim, Generalized Inverses in Numerical Solutions of CSIE, Comm. of KMS, 13, No.4, 875-888 (1998).
S. Kim, Solving Singular Integral Equations Using Regularization Methods, Comp. & Math. with Applic., 35(10), (1998).
S. Kim, Numerical Methods using Trust-Region Approach for Solving Nonlinear Ill-Posed Problems, Comm. Kor. Math, 11(4), 1147-1157 (1996).
S. Kim and R. P. Tewarson, Computational Techniques for Inverse Problems in Kidney Modeling, Appl. Math. Letters, 9(3),77-81 (1996).
S. Kim, A Kantorovich-type Convergence Analysis of Quasi-Gauss-Newton Methods, J. Kor. Math, 33(4), 865-878, (1996).
S. Kim and R. P. Tewarson, On the Convergence of quasi-Gauss-Newton Methods for Nonlinear Problems, Computers Math. Applic., 29(3), 27-38 (1995).
S. Kim, A Quasi-Newton Algorithm Using Directional Derivatives for Nonlinear Equations, Comm. Kor. Math. Soc., 9(2) (1994).
S. Kim, On Improving Gauss-Newton Methods for Nonlinear Equations, Comm. Kor. Math. Soc., 9(3), 739-751 (1994).
S. Kim and R. P. Tewarson, A Quasi-Newton Method for Solving Nonlinear Algebraic Equations, Comp. & Math. with Applic., 4(4), 93-97 (1992).
R. P. Tewarson, S. Kim and J. L. Stephenson, Using Quasi-Newton Methods for Kidney Modeling Equations, Appl. Math. Letters, 3(2), 93-95 (1990).

2002-03-18

Sunyoung Kim