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For convex optimization problems Bregman divergences appear as regret functions. Such regret functions can be defined on any convex set but if a sufficiency condition is added the regret function must be proportional to information…

Information Theory · Computer Science 2017-02-20 Peter Harremoës

Logarithmic score and information divergence appear in both information theory, statistics, statistical mechanics, and portfolio theory. We demonstrate that all these topics involve some kind of optimization that leads directly to the use…

Statistics Theory · Mathematics 2015-07-28 Peter Harremoës

Recently it has been demonstrated that the Shannon entropy or the von Neuman entropy are the only entropy functions that generate a local Bregman divergences as long as the state space has rank 3 or higher. In this paper we will study the…

Mathematical Physics · Physics 2018-05-07 Peter Harremoës

We extend Berge's Maximum Theorem to allow for incomplete preferences. We first provide a simple version of the Maximum Theorem for convex feasible sets and a fixed preference. Then, we show that if, in addition to the traditional…

Theoretical Economics · Economics 2021-11-17 Leandro Gorno , Alessandro Rivello

We show that the strongly symmetric spectral convex compact sets are precisely the normalized state spaces of finite-dimensional simple Euclidean Jordan algebras and the simplices. Spectrality is the property that every state has a convex…

Mathematical Physics · Physics 2019-04-09 Howard Barnum , Joachim Hilgert

We study maximum-entropy inference for finite-dimensional quantum states under linear moment constraints. Given expectation values of finitely many observables, the feasible set of states is convex but typically non-unique. The…

Quantum Physics · Physics 2025-10-27 James Tian

We revisit the mathematical foundations of proper scoring rules (PSRs) and Bregman divergences and present their characteristic properties in a unified theoretical framework. In many situations it is preferable not to generate a PSR…

Statistics Theory · Mathematics 2015-09-11 Evgeni Y. Ovcharov

We show that a simple geometric result suffices to derive the form of the optimal solution in a large class of finite and infinite-dimensional maximum entropy problems concerning probability distributions, spectral densities and covariance…

Optimization and Control · Mathematics 2012-12-24 Michele Pavon , Augusto Ferrante

Bowen showed that a continuous expansive map with specification has a unique measure of maximal entropy. We show that the conclusion remains true under weaker non-uniform versions of these hypotheses. To this end, we introduce the notions…

Dynamical Systems · Mathematics 2019-02-20 Vaughn Climenhaga , Daniel J. Thompson

The problem to maximize the information divergence from an exponential family is generalized to the setting of Bregman divergences and suitably defined Bregman families.

Information Theory · Computer Science 2021-11-02 Johannes Rauh , František Matúš

We describe the structure of the bijective transformations on the set of density operators which preserve the Bregman $f$-divergence for an arbitrary differentiable strictly convex function $f.$ Furthermore, we determine the preservers of…

Mathematical Physics · Physics 2016-08-09 Dániel Virosztek

Bregman divergences are a class of distance-like comparison functions which play fundamental roles in optimization, statistics, and information theory. One important property of Bregman divergences is that they cause two useful formulations…

Information Theory · Computer Science 2025-01-07 Philip S. Chodrow

We apply the Principle of Maximum Entropy to the study of a general class of deterministic fractal sets. The scaling laws peculiar to these objects are accounted for by means of a constraint concerning the average content of information in…

Statistical Mechanics · Physics 2015-06-25 R. Pastor-Satorras , J. Wagensberg

We consider perturbations of a large Jordan matrix, either random and small in norm or of small rank. In both cases we show that most of the eigenvalues of the perturbed matrix are very close to a circle with centre at the origin. In the…

Spectral Theory · Mathematics 2007-05-23 E B Davies , Mildred Hager

A special class of Jordan algebras over a field $F$ of characteristic zero is considered. Such an algebra consists of an $r$-dimensional subspace of the vector space of all square matrices of a fixed order $n$ over $F$. It contains the…

Combinatorics · Mathematics 2019-11-15 Mikhail Klin , Mikhail Muzychuk , Sven Reichard

In this paper we determine those bijective maps of the set of all positive definite $n\times n$ complex matrices which preserve a given Bregman divergence corresponding to a differentiable convex function that satisfies certain conditions.…

Functional Analysis · Mathematics 2016-02-25 Lajos Molnár , József Pitrik , Dániel Virosztek

We first introduce the class of strictly quasiconvex and strictly quasiconcave Jensen divergences which are oriented (asymmetric) distances, and study some of their properties. We then define the strictly quasiconvex Bregman divergences as…

Information Theory · Computer Science 2019-10-08 Frank Nielsen , Gaëtan Hadjeres

We formulate a notion of "geometric reductivity" in an abstract categorical setting which we refer to as adequacy. The main theorem states that the adequacy condition implies that the ring of invariants is finitely generated. This result…

Algebraic Geometry · Mathematics 2010-11-10 Jarod Alper , A. J. de Jong

We derive optimal estimates for the Bergman kernel and the Bergman metric for certain model domains in $\mathbb{C}^2$ near boundary points that are of infinite type. Being unbounded models, these domains obey certain geometric constraints…

Complex Variables · Mathematics 2021-03-25 Gautam Bharali

In 1934, Jordan et al. gave a necessary algebraic condition, the Jordan identity, for a sensible theory of quantum mechanics. All but one of the algebras that satisfy this condition can be described by Hermitian matrices over the complexes…

Rings and Algebras · Mathematics 2011-01-04 Corinne A. Manogue , Tevian Dray
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