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Lieb's concavity theorem, matrix geometric means, and semidefinite optimization

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arxiv 1512.03401 v3 pith:ZTCHRUIP submitted 2015-12-10 math.OC quant-ph

classification math.OCquant-ph
keywords liebmatrixsemidefiniteformulationgeometricmeansadmitsconcave
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abstract

A famous result of Lieb establishes that the map $(A,B) \mapsto \text{tr}\left[K^* A^{1-t} K B^t\right]$ is jointly concave in the pair $(A,B)$ of positive definite matrices, where $K$ is a fixed matrix and $t \in [0,1]$. In this paper we show that Lieb's function admits an explicit semidefinite programming formulation for any rational $t \in [0,1]$. Our construction makes use of a semidefinite formulation of weighted matrix geometric means. We provide an implementation of our constructions in Matlab.

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  1. Accelerated optimization of measured relative entropies

    quant-ph 2025-11 conditional novelty 5.0 of 10

    Measured relative entropies can be computed by Nesterov accelerated gradient descent/ascent because their variational objective functions are smooth and strongly convex/concave.

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