On Positiveness of Matrix-Valued Polynomials and Robust Semidefinite Programming
This report is devoted to the study of robust semidefinite programming. We show that to the issue of computing the worst-case optimal value of semidefinite programs depending polynomially upon a finite number of bounded scalar parameters, one may associate a countable family of standard semidefinite programs, whose optimal values converge monotonically
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Bias-Reduced Localization for Drone Swarm Based on Sensor Selection. [PDF]
Wu B, Shen B, Zhang Y, Yang L, Wang Z.
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On Tractable Convex Relaxations of Standard Quadratic Optimization Problems under Sparsity Constraints. [PDF]
Bomze I, Peng B, Qiu Y, Yıldırım EA.
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Weighted Sum-Rate Maximization and Task Completion Time Minimization for Multi-Tag MIMO Symbiotic Radio Networks. [PDF]
Suo L, Wang D, Zhou W, Peng X.
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Matrix discrepancy and the log-rank conjecture. [PDF]
Sudakov B, Tomon I.
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Joint beamforming and transmit power control for maximizing uplink communication rate of active IRS-assisted ISAC system. [PDF]
Wang J, Lu S.
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Positive semidefinite matrix inequalities for nonnegative functions
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MaxComp: Predicting single-cell chromatin compartments from 3D chromosome structures. [PDF]
Zhan Y, Musella F, Alber F.
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Accelerating Variable Cell Shape Molecular Dynamics with a Position-Dependent Mass Matrix. [PDF]
Sommer-Jörgensen M +2 more
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Axiomatic Approach to Measures of Total Correlations. [PDF]
Moraes GL, Angelo RM, Costa ACS.
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