Some Families of Jensen-like Inequalities with Application to Information Theory.

It is well known that the traditional Jensen inequality is proved by lower bounding the given convex function, f(x), by the tangential affine function that passes through the point (E{X},f(E{X})), where E{X} is the expectation of the random variable X. While this tangential affine function yields the tightest lower bound among all lower bounds induced by affine functions that are tangential to f, it turns out that when the function f is just part of a more complicated expression whose expectation is to be bounded, the tightest lower bound might belong to a tangential affine function that passes through a point different than (E{X},f(E{X})). In this paper, we take advantage of this observation by optimizing the point of tangency with regard to the specific given expression in a variety of cases and thereby derive several families of inequalities, henceforth referred to as "Jensen-like" inequalities, which are new to the best knowledge of the author. The degree of tightness and the potential usefulness of these inequalities is demonstrated in several application examples related to information theory.

Logical Entropy of Information Sources.

In this paper, we present the concept of the logical entropy of order m, logical mutual information, and the logical entropy for information sources. We found upper and lower bounds for the logical entropy of a random variable by using convex functions. We show that the logical entropy of the joint distributions X1 and X2 is always less than the sum of the logical entropy of the variables X1 and X2. We define the logical Shannon entropy and logical metric permutation entropy to an information system and examine the properties of this kind of entropy. Finally, we examine the amount of the logical metric entropy and permutation logical entropy for maps.

Quantum Estimates for Different Type Intequalities through Generalized Convexity.

This article estimates several integral inequalities involving (h-m)-convexity via the quantum calculus, through which Important integral inequalities including Simpson-like, midpoint-like, averaged midpoint-trapezoid-like and trapezoid-like are extended. We generalized some quantum integral inequalities for q-differentiable (h-m)-convexity. Our results could serve as the refinement and the unification of some classical results existing in the literature by taking the limit q→1-.

An Objective Prior from a Scoring Rule.

In this paper, we introduce a novel objective prior distribution levering on the connections between information, divergence and scoring rules. In particular, we do so from the starting point of convex functions representing information in density functions. This provides a natural route to proper local scoring rules using Bregman divergence. Specifically, we determine the prior which solves setting the score function to be a constant. Although in itself this provides motivation for an objective prior, the prior also minimizes a corresponding information criterion.

Inequalities for Jensen-Sharma-Mittal and Jeffreys-Sharma-Mittal Type f-Divergences.

In this paper, we introduce new divergences called Jensen-Sharma-Mittal and Jeffreys-Sharma-Mittal in relation to convex functions. Some theorems, which give the lower and upper bounds for two new introduced divergences, are provided. The obtained results imply some new inequalities corresponding to known divergences. Some examples, which show that these are the generalizations of Rényi, Tsallis, and Kullback-Leibler types of divergences, are provided in order to show a few applications of new divergences.

New duality results for evenly convex optimization problems.

We present new results on optimization problems where the involved functions are evenly convex. By means of a generalized conjugation scheme and the perturbation theory introduced by Rockafellar, we propose an alternative dual problem for a general optimization one defined on a separated locally convex topological space. Sufficient conditions for converse and total duality involving the even convexity of the perturbation function and c-subdifferentials are given. Formulae for the c-subdifferential and biconjugate of the objective function of a general optimization problem are provided, too. We also characterize the total duality by means of the saddle-point theory for a notion of Lagrangian adapted to the considered framework.

Robust Multicategory Support Matrix Machines.

We consider the classification problem when the input features are represented as matrices rather than vectors. To preserve the intrinsic structures for classification, a successful method is the Support Matrix Machine (SMM) in [19], which optimizes an objective function with a hinge loss plus a so-called spectral elastic net penalty. However, the issues of extending SMM to multicategory classification still remain. Moreover, in practice, it is common to see the training data contaminated by outlying observations, which can affect the robustness of existing matrix classification methods. In this paper, we address these issues by introducing a robust angle-based classifier, which boils down binary and multicategory problems to a unified framework. Benefitting from the use of truncated hinge loss functions, the proposed classifier achieves certain robustness to outliers. The underlying optimization model becomes nonconvex, but admits a natural DC (difference of two convex functions) representation. We develop a new and efficient algorithm by incorporating the DC algorithm and primal-dual first-order methods together. The proposed DC algorithm adaptively chooses the accuracy of the subproblem at each iteration while guaranteeing the overall convergence of the algorithm. The use of primal-dual methods removes a natural complexity of the linear operator in the subproblems and enables us to use the proximal operator of the objective functions, and matrix-vector operations. This advantage allows us to solve large-scale problems efficiently. Theoretical and numerical results indicate that for problems with potential outliers, our method can be highly competitive among existing methods.

New Estimations for Shannon and Zipf-Mandelbrot Entropies.

The main purpose of this paper is to find new estimations for the Shannon and Zipf-Mandelbrot entropies. We apply some refinements of the Jensen inequality to obtain different bounds for these entropies. Initially, we use a precise convex function in the refinement of the Jensen inequality and then tamper the weight and domain of the function to obtain general bounds for the Shannon entropy (SE). As particular cases of these general bounds, we derive some bounds for the Shannon entropy (SE) which are, in fact, the applications of some other well-known refinements of the Jensen inequality. Finally, we derive different estimations for the Zipf-Mandelbrot entropy (ZME) by using the new bounds of the Shannon entropy for the Zipf-Mandelbrot law (ZML). We also discuss particular cases and the bounds related to two different parametrics of the Zipf-Mandelbrot entropy. At the end of the paper we give some applications in linguistics.

The second and third Hankel determinants for starlike and convex functions associated with Three-Leaf function.

In this paper, we give sharp bounds of the Hankel determinant H 2 ( 3 ) ( f ) for the coefficients of functions in the class of starlike functions related to a domain that is like three leaves. We also give sharp bounds for the Hankel determinants H 3 ( 1 ) ( f ) and H 2 ( 3 ) ( f ) for the coefficients of functions in the class of convex functions related to the three-leaf-like domain.

The Hadwiger theorem on convex functions, III: Steiner formulas and mixed Monge-Ampère measures.

A complete family of functional Steiner formulas is established. As applications, an explicit representation of functional intrinsic volumes using special mixed Monge-Ampère measures and a new version of the Hadwiger theorem on convex functions are obtained.

An extension of the proximal point algorithm beyond convexity.

We introduce and investigate a new generalized convexity notion for functions called prox-convexity. The proximity operator of such a function is single-valued and firmly nonexpansive. We provide examples of (strongly) quasiconvex, weakly convex, and DC (difference of convex) functions that are prox-convex, however none of these classes fully contains the one of prox-convex functions or is included into it. We show that the classical proximal point algorithm remains convergent when the convexity of the proper lower semicontinuous function to be minimized is relaxed to prox-convexity.

Improving coarse-grained models of protein folding through weighting of polar-polar/hydrophobic-hydrophobic interactions into crowded spaces.

Herein were tested 7 hydrophobic-polar sequences in two types of 2D-square space lattices, homogeneous and correlated, the latter simulating molecular crowding included as a geometric boundary restriction. Optimization of 2D structures was carried out using a variant of Dill's model, inspired by convex function, taking into account both hydrophobic (Dill's model) and polar interactions, including more structural information to reach better folding solutions. While using correlated networks, degrees of freedom in the folding of sequences were limited; as a result in all cases, more successful structural trials were found in comparison to a homogeneous lattice. The majority of employed sequences were designed by our workgroup, two of them were folded with other approaches, and another is a modified version of a previous sequence, initial forms of the other two have been employed but without taking into account polar-polar contributions. Three of them are newly proposed, intended to test the conjoint hydrophobic-hydrophobic and polar-polar contributions in crowded spaces. One sequence turned out to be the most difficult of the seven folded, this perhaps due to intrinsic (i) degrees of freedom and (ii) motifs of the expected 2D HP structure. Meanwhile two-sequence, although optimal folding was not achieved for neither of the two approaches, folding with correlated network approach not only produced better results than homogeneous space, but for them the best values found with crowding were very close to the expected optimal fitness. In general, five sequences were better folded with medium lattice units for correlated media; instead, another two sequences were better folded with a bit larger degree of lattice unit, revealing that depending on the degrees of freedom and particular folding, motifs in each sequence would require tuned crowding to achieve better folding. Therefore, the main goal herein was to obtain a modified 2D HP lattice model to mimic folding of proteins or secondary structures, like ß-sheets, taking into account both hydrophobic-hydrophobic and polar-polar interactions, and fold them in a crowded environment. This simple but enough construction would be conducted to determine the needed information to fold sequences in a sort of a minimal but complete heuristic model. Finally, we claim that all folded sequences into crowded spaces achieve better results than homogeneous ones.

ESTROGEN RECEPTOR EXPRESSION ON BREAST CANCER PATIENTS' SURVIVAL UNDER SHAPE RESTRICTED COX REGRESSION MODEL.

For certain subtypes of breast cancer, study findings show that their level of estrogen receptor expression is associated with their risk of cancer death, and also suggests a non-linear effect on the hazard of death. A flexible form of the proportional hazards model, λ(t∣x, z ) = λ(t) exp( z T ß )q(x), is desirable to facilitate a rich class of covariate effect on a survival outcome to provide meaningful insight, where the functional form of q(x) is not specified except for its shape. Prior biologic knowledge on the shape of the underlying distribution of the covariate effect in regression models can be used to enhance statistical inference. Despite recent progress, major challenges remain for the semiparametric shape-restricted inference due to lack of practical and efficient computational algorithms to accomplish non-convex optimization. We propose an alternative algorithm to maximize the full log-likelihood with two sets of parameters iteratively under monotone constraints. The first set consists of the non-parametric estimation of the monotone-restricted function q(x), while the second set includes estimating the baseline hazard function and other covariate coefficients. The iterative algorithm in conjunction with the pool-adjacent-violators algorithm makes the computation efficient and practical. The Jackknife resampling effectively reduces the estimator bias, when sample size is small. Simulations show that the proposed method can accurately capture the underlying shape of q(x), and outperforms the estimators when q(x) in the Cox model is mis-specified. We apply the method to model the effect of estrogen receptor on breast cancer patients' survival.

Some majorization integral inequalities for functions defined on rectangles.

In this paper, we first prove an integral majorization theorem related to integral inequalities for functions defined on rectangles. We then apply the result to establish some new integral inequalities for functions defined on rectangles. The results obtained are generalizations of weighted Favard's inequality, which also provide a generalization of the results given by Maligranda et al. (J. Math. Anal. Appl. 190:248-262, 1995) in an earlier paper.

The Choquet integral of log-convex functions.

In this paper we investigate the upper bound and the lower bound of the Choquet integral for log-convex functions. Firstly, for a monotone log-convex function, we state the similar Hadamard inequality of the Choquet integral in the framework of distorted measure. Secondly, we estimate the upper bound of the Choquet integral for a general log-convex function, respectively, in the case of distorted Lebesgue measure and in the non-additive measure. Finally, we present Jensen's inequality of the Choquet integral for log-convex functions, which can be used to estimate the lower bound of this kind when the non-additive measure is concave. We provide some examples in the framework of the distorted Lebesgue measure to illustrate all the results.

Hermite-Hadamard type inequalities for fractional integrals via Green's function.

In the article, we establish the left Riemann-Liouville fractional Hermite-Hadamard type inequalities and the generalized Hermite-Hadamard type inequalities by using Green's function and Jensen's inequality, and present several new Hermite-Hadamard type inequalities for a class of convex as well as monotone functions.

General fractional integral inequalities for convex and m-convex functions via an extended generalized Mittag-Leffler function.

In this paper some new general fractional integral inequalities for convex and m-convex functions by involving an extended Mittag-Leffler function are presented. These results produce inequalities for several kinds of fractional integral operators. Some interesting special cases of our main results are also pointed out.

Weighted version of Hermite-Hadamard type inequalities for geometrically quasi-convex functions and their applications.

This paper presents new weighted Hermite-Hadamard type inequalities for a new class of convex functions which are known as geometrically quasi-convex functions. Some applications of these results to special means of positive real numbers have also been presented. These findings have been proved to be useful for researchers working in the fields of numerical analysis and mathematical inequalities.

On approximation and energy estimates for delta 6-convex functions.

The smooth approximation and weighted energy estimates for delta 6-convex functions are derived in this research. Moreover, we conclude that if 6-convex functions are closed in uniform norm, then their third derivatives are closed in weighted [Formula: see text]-norm.

Hermite-Hadamard inequalities and their applications.

New Hermite-Hadamard type inequalities are established. Some corresponding examples are also discussed in detail.