On convex iterative roots of non-monotonic mappings

Let I be an interval. We consider the non-monotonic convex self-mappings \(f:I\rightarrow I\) such that \(f^2\) is convex. They have the property that all iterates \(f^n\) are convex. In the class of these mappings we study three families of functions possessing convex iterative roots. A function f is said to be iteratively convex if f possesses convex iterative roots of all orders. A mapping f is said to be dyadically convex if for every \(n\ge 2\) there exists a convex iterative root \(f^{1/2^n}\) of order \(2^n\) and the sequence \(\{f^{1/2^n}\}\) satisfies the condition of compatibility, that is \( f^{1/2^n}\circ f^{1/2^n}= f^{1/2^{n-1}}.\) A function f is said to be flowly convex if it possesses a convex semi-flow of f, that is a family of convex functions \(\{f^t,t>0\}\) such that \(f^t\circ f^s=f^{t+s}, \ \ t,s >0\) and \(f^1=f\). We show the relations among these three types of convexity and we determine all convex iterative roots of non-monotonic functions.     

A technique to derive the analytical form of convex envelopes for some bivariate functions

In the recent paper (Locatelli and Schoen in Math Program, 2013) it is shown that the value of the convex envelope of some bivariate functions over polytopes can be computed by solving a continuously differentiable convex problem. In this paper we show how this result can be exploited to derive in some cases the analytical form of the envelope. The technique is illustrated through two examples.     

The $${\varphi}$$-Brunn–Minkowski inequality

For strictly increasing concave functions \({\varphi}\) whose inverse functions are log-concave, the \({\varphi}\)-Brunn–Minkowski inequality for planar convex bodies is established. It is shown that for convex bodies in \({\mathbb{R}^n}\) the \({\varphi}\)-Brunn–Minkowski is equivalent to the \({\varphi}\)-Minkowski mixed volume inequalities.     

Finitely Generated Submodules in the Module of Entire Functions Determined by Restrictions on the Indicator Function

We study finitely generated submodules in the module $P$ of entire functions bounded by a system of $\rho $ -trigonometrically convex weights majorized by a given $\rho $ -trigonometrically convex function. Sufficient conditions for the ampleness of a finitely generated submodule in terms of the relative position of the zeros of its generators are obtained. Using these conditions, we prove that each ample submodule in $P$ is generated by two (possibly, coinciding) functions.     

A fast algorithm to sample the number of vertexes and the area of the random convex hull on the unit square

We propose an algorithm to sample the area of the smallest convex hull containing \(n\) sample points uniformly distributed over unit square. To do it, we introduce a new coordinate system for the position of vertexes and re-write joint distribution of the number of vertexes and their locations in the new coordinate system. The proposed algorithm is much faster than existing procedure and has a computational complexity on the order of \(O(T)\) , where \(T\) is the number of vertexes. Using the proposed algorithm, we numerically investigate the asymptotic behavior of functionals of the random convex hull. In addition, we apply it to finding pairs of stocks where the returns are dependent on each other on the New York Stock Exchange.     

Hankel Operators on Holomorphic Hardy–Orlicz Spaces

We characterize the symbols of Hankel operators that extend into bounded operators from the Hardy–Orlicz ${\mathcal H^{\Phi_1}(\mathbb B^n)}$ into ${\mathcal H^{\Phi_2}(\mathbb B^n)}$ in the unit ball of ${\mathbb C^n}$ , in the case where the growth functions ${\Phi_1}$ and ${\Phi_2}$ are either concave or convex. The case where the growth functions are both concave has been studied by Bonami and Sehba. We also obtain several weak factorization theorems for functions in ${\mathcal H^{\Phi}(\mathbb B^n)}$ , with concave growth function, in terms of products of Hardy–Orlicz functions with convex growth functions.     

On convex relaxations for quadratically constrained quadratic programming

We consider convex relaxations for the problem of minimizing a (possibly nonconvex) quadratic objective subject to linear and (possibly nonconvex) quadratic constraints. Let $\mathcal{F }$ denote the feasible region for the linear constraints. We first show that replacing the quadratic objective and constraint functions with their convex lower envelopes on $\mathcal{F }$ is dominated by an alternative methodology based on convexifying the range of the quadratic form $\genfrac(){0.0pt}{}{1}{x}\genfrac(){0.0pt}{}{1}{x}^T$ for $x\in \mathcal{F }$ . We next show that the use of ?? $\alpha $ BB?? underestimators as computable estimates of convex lower envelopes is dominated by a relaxation of the convex hull of the quadratic form that imposes semidefiniteness and linear constraints on diagonal terms. Finally, we show that the use of a large class of D.C. (??difference of convex??) underestimators is dominated by a relaxation that combines semidefiniteness with RLT constraints.     

Third Hankel Determinants for Subclasses of Univalent Functions

The main aim of this paper is to discuss the third Hankel determinants for three classes: \(S^*\) of starlike functions, \(\mathcal {K}\) of convex functions and \(\mathcal {R}\) of functions whose derivative has a positive real part. Moreover, the sharp results for twofold and threefold symmetric functions from these classes are obtained.     

On the strongest locally convex Lebesgue topology on Orlicz spaces

Let L^φ be an Orlicz space defined by an Orlicz function φ taking only finite values with ${{\rm lim\ inf}\atop {u\rightarrow \infty}}{\varphi(u)\over u} >0$ (not necessarily convex) over a complete, σ-finite and atomless measure space and let L^φ)_n ^～ stand for the order continuous dual of Lφ. Then the strongest locally convex Lebesgue topology τ on Lφ (= the Mackey topology τ(Lφ, (Lφ)_n ^～) is equal to the restriction of the strongest Lebesgue topology η on $L^{\overline\varphi}$ , where $\overline\varphi$ is the convex minorant of φ and τ is generated by a family of norms defined by some convex Orlicz functions.     

Continuity of solutions of a problem in the calculus of variations

We study the problem of minimizing ${\int_{\Omega} L(x,u(x),Du(x))\,{\rm d}x}$ over the functions ${u\in W^{1,p}(\Omega)}$ that assume given boundary values ${\phi}$ on ???. We assume that L(x, u, Du)?=?F(Du)?+?G(x, u) and that F is convex. We prove that if ${\phi}$ is continuous and ?? is convex, then any minimum u is continuous on the closure of ??. When ?? is not convex, the result holds true if F(Du)?=?f(|Du|). Moreover, if ${\phi}$ is Lipschitz continuous, then u is H?lder continuous.     

The Set of Packing and Covering Densities of Convex Disks

For every convex disk $K$ (a convex compact subset of the plane, with non-void interior), the packing density $\delta (K)$ and covering density ${\vartheta (K)}$ form an ordered pair of real numbers, i.e., a point in $\mathbb{R }^2$ . The set $\varOmega $ consisting of points assigned this way to all convex disks is the subject of this article. A few known inequalities on $\delta (K)$ and ${\vartheta (K)}$ jointly outline a relatively small convex polygon $P$ that contains $\varOmega $ , while the exact shape of $\varOmega $ remains a mystery. Here we describe explicitly a leaf-shaped convex region $\Lambda $ contained in $\varOmega $ and occupying a good portion of $P$ . The sets $\varOmega _T$ and $\varOmega _L$ of translational packing and covering densities and lattice packing and covering densities are defined similarly, restricting the allowed arrangements of $K$ to translated copies or lattice arrangements, respectively. Due to affine invariance of the translative and lattice density functions, the sets $\varOmega _T$ and $\varOmega _L$ are compact. Furthermore, the sets $\varOmega , \,\varOmega _T$ and $\varOmega _L$ contain the subsets $\varOmega ^\star , \,\varOmega _T^\star $ and $\varOmega _L^\star $ respectively, corresponding to the centrally symmetric convex disks $K$ , and our leaf $\Lambda $ is contained in each of $\varOmega ^\star , \,\varOmega _T^\star $ and $\varOmega _L^\star $ .     

Levi Extension Theorems for Meromorphic Functions of Weak Type in Infinite Dimension

The aim of paper is to give some results, that prepare for studying the problem on cross theorems for separately \((\cdot , W)\)-meromorphic functions. Some general versions of extension theorem of Levi type are extended to the classes of meromorphic functions f on \(D \times (\Delta _r {\setminus } \overline{\Delta })\) with values in a locally convex space F. Here, the function f is assumed that, for each \(z \in D_*,\) the function \(f_z = f(z, \cdot )\) has a (F, W)-meromorphic extension to \(\Delta _r,\) where F is either a locally (or sequentially) complete locally convex space or a Fréchet space, the space \(W \subseteq F'\) is separating (or determines boundedness), \(\Delta _r = \{\lambda \in {\mathbb C}: |\lambda | < r\}\) with \(r > 1, \Delta = \Delta _1\) and D is either a domain in \({\mathbb C}^n\) or a balanced domain in a Fréchet space containing a non-pluripolar balanced convex compact subset, \(D_*\) is dense in D.     

Graver basis and proximity techniques for block-structured separable convex integer minimization problems

We consider $N$ -fold $4$ -block decomposable integer programs, which simultaneously generalize $N$ -fold integer programs and two-stage stochastic integer programs with $N$ scenarios. In previous work (Hemmecke et al. in Integer programming and combinatorial optimization. Springer, Berlin, 2010), it was proved that for fixed blocks but variable  $N$ , these integer programs are polynomial-time solvable for any linear objective. We extend this result to the minimization of separable convex objective functions. Our algorithm combines Graver basis techniques with a proximity result (Hochbaum and Shanthikumar in J. ACM 37:843–862,1990), which allows us to use convex continuous optimization as a subroutine.     

LR Characterization of Chirotopes of Finite Planar Families of Pairwise Disjoint Convex bodies

We extend the classical LR characterization of chirotopes of finite planar families of points to chirotopes of finite planar families of pairwise disjoint convex bodies: a map $\chi $ χ on the set of 3-subsets of a finite set $I$ I is a chirotope of finite planar families of pairwise disjoint convex bodies if and only if for every 3-, 4-, and 5-subset $J$ J of $I$ I the restriction of $\chi $ χ to the set of 3-subsets of $J$ J is a chirotope of finite planar families of pairwise disjoint convex bodies. Our main tool is the polarity map, i.e., the map that assigns to a convex body the set of lines missing its interior, from which we derive the key notion of arrangements of double pseudolines, introduced for the first time in this paper.     

On representations of the feasible set in convex optimization

We consider the convex optimization problem \({\min_{\mathbf{x}} \{f(\mathbf{x}): g_j(\mathbf{x})\leq 0, j=1,\ldots,m\}}\) where f is convex, the feasible set \({\mathbf{K}}\) is convex and Slater’s condition holds, but the functions g _j ’s are not necessarily convex. We show that for any representation of \({\mathbf{K}}\) that satisfies a mild nondegeneracy assumption, every minimizer is a Karush-Kuhn-Tucker (KKT) point and conversely every KKT point is a minimizer. That is, the KKT optimality conditions are necessary and sufficient as in convex programming where one assumes that the g _j ’s are convex. So in convex optimization, and as far as one is concerned with KKT points, what really matters is the geometry of \({\mathbf{K}}\) and not so much its representation.     

Multivariate McCormick relaxations

McCormick (Math Prog 10(1):147–175, 1976) provides the framework for convex/concave relaxations of factorable functions, via rules for the product of functions and compositions of the form \(F\circ f\) , where \(F\) is a univariate function. Herein, the composition theorem is generalized to allow multivariate outer functions \(F\) , and theory for the propagation of subgradients is presented. The generalization interprets the McCormick relaxation approach as a decomposition method for the auxiliary variable method. In addition to extending the framework, the new result provides a tool for the proof of relaxations of specific functions. Moreover, a direct consequence is an improved relaxation for the product of two functions, at least as tight as McCormick’s result, and often tighter. The result also allows the direct relaxation of multilinear products of functions. Furthermore, the composition result is applied to obtain improved convex underestimators for the minimum/maximum and the division of two functions for which current relaxations are often weak. These cases can be extended to allow composition of a variety of functions for which relaxations have been proposed.     

Characteristic functions as distributional boundary values of analytic functions in tube domains

We propose necessary and sufficient conditions for a complex-valued function f on \( {{\mathbb{R}}^n} \) to be a characteristic function of a probability measure. Certain analytic extensions of f to tubular domains in \( {{\mathbb{C}}^n} \) are studied. In order to extend the class of functions under study, we also consider the case where f is a generalized function (distribution). The main result is given in terms of completely monotonic functions on convex cones in \( {{\mathbb{R}}^n} \) .     

Convex underestimators of polynomials

Convex underestimators of a polynomial on a box. Given a non convex polynomial ${f\in \mathbb{R}[{\rm x}]}$ and a box ${{\rm B}\subset \mathbb{R}^n}$ , we construct a sequence of convex polynomials ${(f_{dk})\subset \mathbb{R}[{\rm x}]}$ , which converges in a strong sense to the “best” (convex and degree-d) polynomial underestimator ${f^{*}_{d}}$ of f. Indeed, ${f^{*}_{d}}$ minimizes the L ₁-norm ${\Vert f-g\Vert_1}$ on B, over all convex degree-d polynomial underestimators g of f. On a sample of problems with non convex f, we then compare the lower bounds obtained by minimizing the convex underestimator of f computed as above and computed via the popular α BB method and some of its other refinements. In most of all examples we obtain significantly better results even with the smallest value of k.