| Sums of squares and semidefinite program relaxations for polynomial optimization problems with structured sparsity H Waki, S Kim, M Kojima, M Muramatsu SIAM Journal on Optimization 17 (1), 218-242, 2006 | 521 | 2006 |
| Algorithm 883: SparsePOP---a sparse semidefinite programming relaxation of polynomial optimization problems H Waki, S Kim, M Kojima, M Muramatsu, H Sugimoto ACM Transactions on Mathematical Software (TOMS) 35 (2), 1-13, 2008 | 215 | 2008 |
| Exact solutions of some nonconvex quadratic optimization problems via SDP and SOCP relaxations S Kim, M Kojima Computational optimization and applications 26, 143-154, 2003 | 204 | 2003 |
| Second order cone programming relaxation of nonconvex quadratic optimization problems S Kim, M Kojima Optimization methods and software 15 (3-4), 201-224, 2001 | 180 | 2001 |
| Sparsity in sums of squares of polynomials M Kojima, S Kim, H Waki Mathematical Programming 103, 45-62, 2005 | 139 | 2005 |
| Exploiting sparsity in linear and nonlinear matrix inequalities via positive semidefinite matrix completion S Kim, M Kojima, M Mevissen, M Yamashita Mathematical programming 129 (1), 33-68, 2011 | 136 | 2011 |
| Exploiting sparsity in SDP relaxation for sensor network localization S Kim, M Kojima, H Waki SIAM Journal on Optimization 20 (1), 192-215, 2009 | 109 | 2009 |
| PHoM–a polyhedral homotopy continuation method for polynomial systems T Gunji, S Kim, M Kojima, A Takeda, K Fujisawa, T Mizutani Computing 73, 57-77, 2004 | 103 | 2004 |
| Generalized Lagrangian duals and sums of squares relaxations of sparse polynomial optimization problems S Kim, M Kojima, H Waki SIAM Journal on Optimization 15 (3), 697-719, 2005 | 93 | 2005 |
| A Lagrangian–DNN relaxation: a fast method for computing tight lower bounds for a class of quadratic optimization problems S Kim, M Kojima, KC Toh Mathematical Programming 156 (1-2), 161-187, 2016 | 75 | 2016 |
| A general framework for convex relaxation of polynomial optimization problems over cones M Kojima, S Kim, H Waki Journal of the Operations Research Society of Japan 46 (2), 125-144, 2003 | 55 | 2003 |
| Correlative sparsity in primal-dual interior-point methods for LP, SDP, and SOCP K Kobayashi, S Kim, M Kojima Applied Mathematics and Optimization 58 (1), 69-88, 2008 | 48 | 2008 |
| Second order cone programming relaxation of a positive semidefinite constraint S Kim, M Kojima, M Yamashita Optimization Methods and Software 18 (5), 535-541, 2003 | 46 | 2003 |
| Computing all nonsingular solutions of cyclic-n polynomial using polyhedral homotopy continuation methods Y Dai, S Kim, M Kojima Journal of Computational and Applied Mathematics 152 (1-2), 83-97, 2003 | 46 | 2003 |
| Algorithm 920: SFSDP: A sparse version of full semidefinite programming relaxation for sensor network localization problems S Kim, M Kojima, H Waki, M Yamashita ACM Transactions on Mathematical Software (TOMS) 38 (4), 1-19, 2012 | 42 | 2012 |
| User’s manual for SparseCoLO: Conversion methods for sparse conic-form linear optimization problems K Fujisawa, S Kim, M Kojima, Y Okamoto, M Yamashita Report B-453, Dept. of Math. and Comp. Sci. Japan, Tech. Rep, 152-8552, 2009 | 39 | 2009 |
| A quadratically constrained quadratic optimization model for completely positive cone programming N Arima, S Kim, M Kojima SIAM Journal on Optimization 23 (4), 2320-2340, 2013 | 38 | 2013 |
| B-475 Lagrangian-Conic Relaxations, Part I: A Unified Framework and Its Applications to Quadratic Optimization Problems N Arima, S Kim, M Kojima, KC Toh | 28 | 2014 |
| Numerical stability of path tracing in polyhedral homotopy continuation methods S Kim, M Kojima Computing 73 (4), 329-348, 2004 | 26 | 2004 |
| Simplified copositive and lagrangian relaxations for linearly constrained quadratic optimization problems in continuous and binary variables N Arima, S Kim, M Kojima Pacific Journal of Optimization 10 (3), 437, 2014 | 25 | 2014 |
Masakazu KojimaTokyo Institute of Technology, Chuo UniversityVahvistettu sähköpostiosoite verkkotunnuksessa is.titech.ac.jp
