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On semidefinite descriptions for convex hulls of quadratic programs
Operations Research LettersQuadratically constrained quadratic programs (QCQPs) are a highly expressive class of nonconvex optimization problems. While QCQPs are NP-hard in general, they admit a natural convex relaxation via the standard semidefinite program (SDP) relaxation.
Fatma Kılınç-Karzan, Alex Wang
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Quantum algorithms and lower bounds for convex optimization [PDF]
Quantum, 2020 While recent work suggests that quantum computers can speed up the solution of semidefinite programs, little is known about the quantum complexity of more general convex optimization.
Shouvanik Chakrabarti +3 more
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EURO Journal on Computational Optimization, 2021 A conic optimization problem is a problem involving a constraint that the optimization variable be in some closed convex cone. Prominent examples are linear programs (LP), second order cone programs (SOCP), semidefinite problems (SDP), and copositive ...
Mirjam Dür, Franz Rendl
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Quadratic Convex Reformulations for Semicontinuous Quadratic Programming [PDF]
SIAM Journal on Optimization, 2017 Summary: We consider in this paper a class of semicontinuous quadratic programming problems, which arises in many real-world applications such as production planning, portfolio selection, and subset selection in regression. We build upon the idea of the quadratic convex reformulation approach, i.e., adding to the original objective function an ...
Baiyi Wu +3 more
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MIMO Dual-Functional Radar-Communication Waveform Design With Peak Average Power Ratio Constraint
IEEE Access, 2021 In this paper, novel Dual-Functional Radar-Communication (DFRC) waveforms with peak average power ratio (PAPR) constraint are designed, which are under the multiple-input multiple-output (MIMO) radar-communication system.
Yujiu Zhao +4 more
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Real-Time Multi-Convex Model Predictive Control for Occlusion-Free Target Tracking With Quadrotors
IEEE Access, 2022 This paper proposes a Model Predictive Control (MPC) algorithm for target tracking amongst static and dynamic obstacles. Our main contribution lies in improving the computational tractability and reliability of the underlying non-convex trajectory ...
Houman Masnavi +3 more
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Convex model predictive control for collision avoidance
IET Control Theory & Applications, 2021 This manuscript proposes a model predictive control for collision avoidance for the regulation problem of deterministic linear systems, which provides a priori guarantees of strong system theoretic properties, such as positive invariance and asymptotic ...
Saša V. Raković +4 more
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Methods for convex and general quadratic programming [PDF]
Mathematical Programming Computation, 2014 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Philip E. Gill, Elizabeth Wong
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Quantum-Inspired Hierarchy for Rank-Constrained Optimization
PRX Quantum, 2022 Many problems in information theory can be reduced to optimizations over matrices, where the rank of the matrices is constrained. We establish a link between rank-constrained optimization and the theory of quantum entanglement.
Xiao-Dong Yu +3 more
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Robust convex quadratically constrained programs [PDF]
Mathematical Programming, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Donald Goldfarb, Garud Iyengar
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