Let $p\in (0,1)$ be a real number and let $n\ge 2$ be an even integer. We determine the largest value $c_n\left(p\right)$ such that the inequality $$\sum _{i=1}^n{\left|a_i\right|}^p\ge c_n\left(p\right)$$ holds for all real numbers $a_1,...,a_n$ which are pairwise distinct and satisfy ${min}_{i\ne j}|a_i-a_j|=1$. Our theorem completes results of Ozeki, Mitrinović-Kalajdžić, and Russell, who found the optimal value $c_n\left(p\right)$ in the case $p>0$ and $n$ odd, and in the case $p\ge 1$ and $n$ even.

Let $P$ be a discrete multidimensional probability distribution over a finite set of variables $N$ which is only partially specified by the requirement that it has prescribed given marginals $\{P_A;A\in 𝒮\}$, where $𝒮$ is a class of subsets of $N$ with $\bigcup 𝒮=N$. The paper deals with the problem of approximating $P$ on the basis of those given marginals. The divergence of an approximation $\widehat{P}$ from $P$ is measured by the relative entropy $H\left(P\right|\widehat{P})$. Two methods for approximating $P$ are compared. One of them uses formerly introduced...

In this paper we are concerned with a class of time-varying discounted Markov decision models ${ℳ}_n$ with unbounded costs $c_n$ and state-action dependent discount factors. Specifically we study controlled systems whose state process evolves according to the equation $x_{n+1}=G_n(x_n,a_n,{\xi }_n),n=0,1,...$, with state-action dependent discount factors of the form ${\alpha }_n(x_n,a_n)$, where $a_n$ and ${\xi }_n$ are the control and the random disturbance at time $n$, respectively. Assuming that the sequences of functions $\left\{{\alpha }_n\right\}$,$\left\{c_n\right\}$ and $\left\{G_n\right\}$ converge, in certain sense, to ${\alpha }_{\infty }$,...

The information divergence of a probability measure $P$ from an exponential family $ℰ$ over a finite set is defined as infimum of the divergences of $P$ from $Q$ subject to $Q\in ℰ$. All directional derivatives of the divergence from $ℰ$ are explicitly found. To this end, behaviour of the conjugate of a log-Laplace transform on the boundary of its domain is analysed. The first order conditions for $P$ to be a maximizer of the divergence from $ℰ$ are presented, including new ones when $P$ is not projectable...