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Positive semidefinite rank
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abstract
Let M be a p-by-q matrix with nonnegative entries. The positive semidefinite rank (psd rank) of M is the smallest integer k for which there exist positive semidefinite matrices $A_i, B_j$ of size $k \times k$ such that $M_{ij} = \text{trace}(A_i B_j)$. The psd rank has many appealing geometric interpretations, including semidefinite representations of polyhedra and information-theoretic applications. In this paper we develop and survey the main mathematical properties of psd rank, including its geometry, relationships with other rank notions, and computational and algorithmic aspects.
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Cited by 1 Pith paper
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Quantum Communication Lower Bounds for Search Problems via Matrix Discrepancy
A matrix-discrepancy argument proves tight one-way quantum lower bounds for collision finding (Ω(N^{1/4})) and for streaming triangle finding (Ω(√Δ_V)) where Boolean-Hidden-Matching reductions fail.
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