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Joint beamforming and transmit power control for maximizing uplink communication rate of active IRS-assisted ISAC system. [PDF]

Sci Rep
Wang J, Lu S.
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A reduced basis decomposition approach to efficient data collection in pairwise comparison studies. [PDF]

Comput Stat
Jiang J, Marsh J, Seymour R.
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A theoretical analysis of mass scaling techniques. [PDF]

Comput Mech
Voet Y, Sande E, Buffa A.
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Quantum Information Flow in Microtubule Tryptophan Networks. [PDF]

Entropy (Basel)
Gassab L, Pusuluk O, Craddock TJA.
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Certified algorithms for equilibrium states of local quantum Hamiltonians. [PDF]

Nat Commun
Fawzi H, Fawzi O, Scalet SO.
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Exact Ground State of Interacting Electrons in Magic Angle Graphene. [PDF]

Commun Math Phys
Becker S, Lin L, Stubbs KD.
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Exploiting sparsity in linear and nonlinear matrix inequalities via positive semidefinite matrix completion

Mathematical Programming, 2010
Optimization problems with nonlinear matrix inequalities, including quadratic and polynomial matrix inequalities, are known as hard problems. They frequently belong to large-scale optimization problems. Exploiting sparsity has been one of the essential tools for solving large-scale optimization problems.
Kim, Sunyoung, Kojima, Masakazu, Mevissen, Martin, Yamashita, Makoto   +3 more
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