arXiv subjects

Ivan Stanojević

Publications and source records attributed to Ivan Stanojević.

5 recordsLinked to original sources

arXiv2023

Convolutional Codes with Optimum Bidirectional Distance Profile

We define the bidirectional distance profile (BDP) of a convolutional code as the minimum of the distance profiles of the code and its corresponding "reverse" code. We present tables of codes with the optimum BDP (OBDP), which minimize the average complexity of bidirectional sequential decoding algorithms. The computer search is accelerated by the facts that optimum distance profile (ODP) codes of larger memory must have ODP codes of smaller memory as their "prefixes", and that OBDP codes can be obtained by "concatenating" ODP and reverse ODP codes of smaller memory. We compare the performance of OBDP codes and other codes by simulation.

cs.IT↗

arXiv2015

Information-Geometric Equivalence of Transportation Polytopes

This paper deals with transportation polytopes in the probability simplex (that is, sets of categorical bivariate probability distributions with prescribed marginals). Information projections between such polytopes are studied, and a sufficient condition is described under which these mappings are homeomorphisms.

cs.IT↗

arXiv2015

On the Entropy of Couplings

In this paper, some general properties of Shannon information measures are investigated over sets of probability distributions with restricted marginals. Certain optimization problems associated with these functionals are shown to be NP-hard, and their special cases are found to be essentially information-theoretic restatements of well-known computational problems, such as the SUBSET SUM and the 3-PARTITION. The notion of minimum entropy coupling is introduced and its relevance is demonstrated in information-theoretic, computational, and statistical contexts. Finally, a family of pseudometrics (on the space of discrete probability distributions) defined by these couplings is studied, in particular their relation to the total variation distance, and a new characterization of the conditional entropy is given.

cs.IT↗

arXiv2013

Some Properties of Rényi Entropy over Countably Infinite Alphabets

In this paper we study certain properties of Rényi entropy functionals $H_α(\mathcal{P})$ on the space of probability distributions over $\mathbb{Z}_+$. Primarily, continuity and convergence issues are addressed. Some properties shown parallel those known in the finite alphabet case, while others illustrate a quite different behaviour of Rényi entropy in the infinite case. In particular, it is shown that, for any distribution $\mathcal P$ and any $r\in[0,\infty]$, there exists a sequence of distributions $\mathcal{P}_n$ converging to $\mathcal{P}$ with respect to the total variation distance, such that $\lim_{n\to\infty}\lim_{α\to{1+}} H_α(\mathcal{P}_n) = \lim_{α\to{1+}}\lim_{n\to\infty} H_α(\mathcal{P}_n) + r$.

cs.IT↗

arXiv2012

On the Hardness of Entropy Minimization and Related Problems

We investigate certain optimization problems for Shannon information measures, namely, minimization of joint and conditional entropies $H(X,Y)$, $H(X|Y)$, $H(Y|X)$, and maximization of mutual information $I(X;Y)$, over convex regions. When restricted to the so-called transportation polytopes (sets of distributions with fixed marginals), very simple proofs of NP-hardness are obtained for these problems because in that case they are all equivalent, and their connection to the well-known \textsc{Subset sum} and \textsc{Partition} problems is revealed. The computational intractability of the more general problems over arbitrary polytopes is then a simple consequence. Further, a simple class of polytopes is shown over which the above problems are not equivalent and their complexity differs sharply, namely, minimization of $H(X,Y)$ and $H(Y|X)$ is trivial, while minimization of $H(X|Y)$ and maximization of $I(X;Y)$ are strongly NP-hard problems. Finally, two new (pseudo)metrics on the space of discrete probability distributions are introduced, based on the so-called variation of information quantity, and NP-hardness of their computation is shown.

cs.IT↗