Maximal entropy in the moment body
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913924083875840 |
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| author | Henrion, Didier |
| author_facet | Henrion, Didier |
| contents | A moment body is a linear projection of the spectraplex, the convex set of trace-one positive semidefinite matrices. Determining whether a given point lies within a given moment body is a problem with numerous applications in quantum state estimation or polynomial optimization. This moment body membership oracle can be addressed with semidefinite programming, for which several off-the-shelf interior-point solvers are available. In this paper, inspired by techniques from quantum information theory, we argue analytically and geometrically that a much more efficient approach consists of minimizing globally a smooth strictly convex log-partition function, dual to a maximum entropy problem. We analyze the curvature properties of this function and we describe a neat geometric pre-conditioning algorithm. A detailed complexity analysis reveals a cubic dependence on the matrix size, similar to a few eigenstructure computations. Basic numerical experiments illustrate that dense (i.e. non-sparse) projections of size 1000 of a dense semidefinite matrix of size 1000-by-1000 can be routinely handled in a few seconds on a standard laptop, thereby moving the main bottleneck in large-scale semidefinite programming almost entirely to efficient gradient storage and manipulation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_02461 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Maximal entropy in the moment body Henrion, Didier Optimization and Control A moment body is a linear projection of the spectraplex, the convex set of trace-one positive semidefinite matrices. Determining whether a given point lies within a given moment body is a problem with numerous applications in quantum state estimation or polynomial optimization. This moment body membership oracle can be addressed with semidefinite programming, for which several off-the-shelf interior-point solvers are available. In this paper, inspired by techniques from quantum information theory, we argue analytically and geometrically that a much more efficient approach consists of minimizing globally a smooth strictly convex log-partition function, dual to a maximum entropy problem. We analyze the curvature properties of this function and we describe a neat geometric pre-conditioning algorithm. A detailed complexity analysis reveals a cubic dependence on the matrix size, similar to a few eigenstructure computations. Basic numerical experiments illustrate that dense (i.e. non-sparse) projections of size 1000 of a dense semidefinite matrix of size 1000-by-1000 can be routinely handled in a few seconds on a standard laptop, thereby moving the main bottleneck in large-scale semidefinite programming almost entirely to efficient gradient storage and manipulation. |
| title | Maximal entropy in the moment body |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2507.02461 |