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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 ...
Wu, Baiyi +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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IEEE Access, 2021 There are the transmission loss of the electric power network, the delay and loss of the heating network, the insufficient utilization of flexible resources such as energy storage in the integrated electric-heat system, which may lead to the imbalance of
Xinrui Liu +3 more
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IEEE Open Journal of the Communications Society, 2021 Full-duplex amplify-and-forward multiple-input multiple-output relaying has been the focus of several recent studies, due to the potential for achieving a higher spectral efficiency and lower latency, together with inherent processing simplicity. However,
Omid Taghizadeh +3 more
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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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EURO Journal on Computational Optimization, 2021 Adjustable robust optimization allows for some variables to depend upon the uncertain data after its realization. However, the uncertainty is often not revealed exactly.
V. Jeyakumar, G. Li, D. Woolnough
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