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In this paper, we study the convex quadratic optimization problem with indicator variables.
Rank-one convexification for sparse regression
Atamtürk, A. and Gómez, A. (2019) · 1901
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A unified approach to mixed-integer optimization: Nonlinear formulations and scalable algorithms
Bertsimas, D., Cory-Wright, R., and Pauphilet, J. (2019) · 1907
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The generalized trust region subproblem: solution complexity and convex hull results
Wang, A. L. and Kılınç-Karzan, F. (2019) · 1907
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Gao, J. and Li, D. (2011) · 1941
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Alfakih, A. Y., Khandani, A., and Wolkowicz, H. (1999) · 1999
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Frangioni, A. and Gentile, C. (2006) · 2006
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Aktürk, M. S., Atamtürk, A., and Gürel, S. (2009) · 2009
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Anstreicher, K. and Burer, S. (2010) · 2010
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Matrix completion with noise
Candes, E. J. and Plan, Y. (2010) · 2010
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Perspective reformulations of mixed integer nonlinear programs with indicator variables
Günlük, O. and Linderoth, J. (2010) · 2010
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Mixed-integer nonlinear programs featuring “on/off” constraints
Hijazi, H., Bonami, P., Cornuéjols, G., and Ouorou, A. (2012) · 2012
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On valid inequalities for quadratic programming with continuous variables and binary indicators
Dong, H. and Linderoth, J. (2013) · 2013
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Cutting-planes for optimization of convex functions over nonconvex sets
Bienstock, D. and Michalka, A. (2014) · 2014
Quadratic cone cutting surfaces for quadratic programs with on–off constraints
Jeon, H., Linderoth, J., and Miller, A. (2017) · 2017
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Minotaur: A mixed-integer nonlinear optimization toolkit
Mahajan, A., Leyffer, S., Linderoth, J., Luedtke, J., and Munson, T. (2017) · 2017
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Quadratic convex reformulations for semicontinuous quadratic programming
Wu, B., Sun, X., Li, D., and Zheng, X. (2017) · 2017
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Strong formulations for quadratic optimization with M-matrices and indicator variables
Atamtürk, A. and Gómez, A. (2018) · 2018
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Sparse and smooth signal estimation: Convexification of ℓ 0 \ell_{0} -formulations
Atamtürk, A., Gómez, A., and Han, S. (2018) · 2018
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On mathematical programming with indicator constraints
Bonami, P., Lodi, A., Tramontani, A., and Wiese, S. (2015) · 2015
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Dong, H., Chen, K., and Linderoth, J. (2015) · 2015
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On handling indicator constraints in mixed integer programming
Belotti, P., Bonami, P., Fischetti, M., Lodi, A., Monaci, M., Nogales-Gómez, A., and Salvagnin, D. (2016) · 2016
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Javanmard, A., Montanari, A., and Ricci-Tersenghi, F. (2016) · 2016
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Valid inequalities for separable concave constraints with indicator variables
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Submodular functions: from discrete to continuous domains
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Exact semidefinite formulations for a class of (random and non-random) nonconvex quadratic programs
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New socp relaxation and branching rule for bipartite bilinear programs
Dey, S. S., Santana, A., and Wang, Y. (2019) · 2019
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Outlier detection in time series via mixed-integer conic quadratic optimization
Gómez, A. (2019) · 2019
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On the tightness of SDP relaxations of QCQPs
Wang, A. L. and Kılınç-Karzan, F. (2019) · 2019
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Safe screening rules for ℓ 0 \ell_{0} -regression
Atamtürk, A. and Gómez, A. (2020) · 2020
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Decompositions of semidefinite matrices and the perspective reformulation of nonseparable quadratic programs
Frangioni, A., Gentile, C., and Hungerford, J. (2020) · 2020
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Strong formulations for conic quadratic optimization with indicator variables
Gómez, A. (2020) · 2020
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Extended formulations for convex hulls of some bilinear functions
Gupte, A., Kalinowski, T., Rigterink, F., and Waterer, H. (2020) · 2020
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