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Variational density matrix optimization using semidefinite programming [PDF]
, 2011 We discuss how semidefinite programming can be used to determine the second-order density matrix directly through a variational optimization. We show how the problem of characterizing a physical or N -representable density matrix leads to matrix ...
Boyd +22 more
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On the Embed and Project Algorithm for the Graph Bandwidth Problem
Mathematics, 2021 The graph bandwidth problem, where one looks for a labeling of graph vertices that gives the minimum difference between the labels over all edges, is a classical NP-hard problem that has drawn a lot of attention in recent decades. In this paper, we focus
Janez Povh
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Distributionally Robust Joint Chance Constrained Problem under Moment Uncertainty
Journal of Applied Mathematics, 2014 We discuss and develop the convex approximation for robust joint chance constraints under uncertainty of first- and second-order moments. Robust chance constraints are approximated by Worst-Case CVaR constraints which can be reformulated by a ...
Ke-wei Ding
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Critical Multipliers in Semidefinite Programming [PDF]
Asia-Pacific Journal of Operational Research, 2020 It was proved in Izmailov and Solodov (2014). Newton-Type Methods for Optimization and Variational Problems, Springer] that the existence of a noncritical multiplier for a (smooth) nonlinear programming problem is equivalent to an error bound condition for the Karush–Kuhn–Thcker (KKT) system without any assumptions.
Zhang, Tianyu, Zhang, Liwei
openaire +3 more sources
A Rank-Two Feasible Direction Algorithm for the Binary Quadratic Programming
Journal of Applied Mathematics, 2013 Based on the semidefinite programming relaxation of the binary quadratic programming, a rank-two feasible direction algorithm is presented. The proposed algorithm restricts the rank of matrix variable to be two in the semidefinite programming relaxation ...
Xuewen Mu, Yaling Zhang
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Variational Quantum Algorithms for Semidefinite Programming [PDF]
QuantumA semidefinite program (SDP) is a particular kind of convex optimization problem with applications in operations research, combinatorial optimization, quantum information science, and beyond.
Dhrumil Patel +2 more
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Sensors, 2018 The energy reading has been an efficient and attractive measure for collaborative acoustic source localization in practical application due to its cost saving in both energy and computation capability.
Yongsheng Yan +4 more
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Multipath Exploitation with Time Reversal Waveform Covariance Matrix for SNR Maximization
Remote Sensing, 2020 Radar target detection has a wide range of applications in the military and civilian remote sensing fields; in particular, the target detection in multipath environments has attracted many scholars’ attention in recent years.
Chao Xiong, Chongyi Fan, Xiaotao Huang
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Exposed faces of semidefinitely representable sets
, 2009 A linear matrix inequality (LMI) is a condition stating that a symmetric matrix whose entries are affine linear combinations of variables is positive semidefinite.
Netzer, Tim +2 more
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Semidefinite Programming Algorithms for 3-D AOA-Based Hybrid Localization
IEEE Open Journal of Signal Processing, 2023 By taking different kinds of measurements at the same time, it may be possible to improve the accuracy of target localization or reduce the number of sensors needed.
Yanbin Zou
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