Maximum likelihood estimation of a log-concave density has attracted considerable attention over the last few years. Several algorithms have been proposed to estimate such a density. Two of those algorithms, an iterative convex minorant and an active set algorithm, are implemented in the R package logcondens. While these algorithms are discussed elsewhere, we describe in this paper the use of t...

holds for all x, y ∈ [,∞), λ ∈ [, ] and for some fixed s ∈ (, ]. The class of s-convex functions in the second sense is usually denoted by K s . It can be easily seen that for s =  s-convexity reduces to ordinary convexity of functions defined on [,∞). It is proved in [] that all functions from K s , s ∈ (, ) are nonnegative. Similarly, a function f : [,∞)→ R is said to be s-concav...

In this paper we show that a finite symmetric game has a pure strategy equilibrium if the payoff functions of players are integrally concave (the negative of the integrally convex functions due to Favati and Tardella [Convexity in nonlinear integer programming, Ricerca Operativa, 1990, 53:3–44]). Since the payoff functions of any two-strategy game are integrally concave, this generalizes the re...

We consider the problem of optimizing convex and concave functions with access to an erroneous zeroth-order oracle. In particular, for a given function x → f(x) we consider optimization when one is given access to absolute error oracles that return values in [f(x) − , f(x) + ] or relative error oracles that return value in [(1− )f(x), (1 + )f(x)], for some > 0. We show stark information theoret...

This paper sheds a new light on the split decomposition theory and T-theory from the viewpoint of convex analysis and polyhedral geometry. By regarding finite metrics as discrete concave functions, Bandelt-Dress’ split decomposition can be derived as a special case of more general decomposition of polyhedral/discrete concave functions introduced in this paper. It is shown that the combinatorics...

Murota et al. have recently developed a theory of discrete convex analysis which concerns M -convex and L-convex functions on jump systems. We introduce here a family of M -concave functions arising naturally from polynomials (over the field of Puiseux series) with prescribed non-vanishing properties. This family contains several of the most studied M -concave functions in the literature. We al...

We consider the problem of maximizing a nondecreasing submodular set function under a matroid constraint. Recently, Calinescu et al. (2007) proposed an elegant framework for the approximation of this problem, which is based on the pipage rounding technique by Ageev and Sviridenko (2004), and showed that this framework indeed yields a (1 − 1/e)-approximation algorithm for the class of submodular...

Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract In this article, some new generalized Hermite-Hadamard type inequalities for functions whose derivatives in absolute values are convex, concave, s-convex in the second sense, and s-concave in the second sense are established.

This short note presents an alternate approximation of concave cost functions used to reflect economies of scale in process design and supply chain optimization problems. To approximate the original concave function, we propose a logarithmic function that is exact and has bounded gradients at zero values in contrast to other approximation schemes. We illustrate the application and advantages of...

An approach to the Shannon and Rényi entropy maximization problems with constraints on the mean and law invariant deviation measure for a random variable has been developed. The approach is based on the representation of law invariant deviation measures through corresponding convex compact sets of nonnegative concave functions. A solution to the problem has been shown to have an alpha-concave d...