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This paper considers the binary Gaussian distribution robust hypothesis testing under aBayesian optimal criterion in the wireless sensor network (WSN). The distribution covariance matrixunder each hypothesis is known, while the distribution mean vector under each hypothesis driftsin an ellipsoidal uncertainty set. Because of the limited bandwidth and energy, we aim at seeking asubset of p out of m sensors such that the best detection performance is achieved. In this setup, theminimax robust sensor selection problem is proposed to deal with the uncertainties of distributionmeans. Following a popular method, minimizing the maximum overall error probability with respectto the selection matrix can be approximated by maximizing the minimum Chernoff distance betweenthe distributions of the selected measurements under null hypothesis and alternative hypothesis tobe detected. Then, we utilize Danskin&rsquo, s theorem to compute the gradient of the objective functionof the converted maximization problem, and apply the orthogonal constraint-preserving gradientalgorithm (OCPGA) to solve the relaxed maximization problem without 0/1 constraints. It is shownthat the OCPGA can obtain a stationary point of the relaxed problem. Meanwhile, we provide thecomputational complexity of the OCPGA, which is much lower than that of the existing greedyalgorithm. Finally, numerical simulations illustrate that, after the same projection and refinementphases, the OCPGA-based method can obtain better solutions than the greedy algorithm-basedmethod but with up to 48.72% shorter runtimes. Particularly, for small-scale problems, the OCPGA-based method is able to attain the globally optimal solution.

Let ℋ be a complex separable Hilbert space, e the collection of orthogonal projections in ℋ, and (X, Ɓ) a Borel space. A function E : Ɓ → e is called a spectral measure if E(X) = I and E(∪ i=1 ∞ A i ) = Σ i=1 ∞ (A i ), for any pairwise disjoint collection A1, A2, A3, …, of sets in Ɓ.

We use that the n-sphere for \(n\ge 2\) is simply-connected to prove the Poincare-Birkhoff-Witt Theorem.

For a symmetric positive semidefinite linear system of equations $$\mathcal{Q}{{\varvec{x}}}= {{\varvec{b}}}$$ , where $${{\varvec{x}}}= (x_1,\ldots ,x_s)$$ is partitioned into s blocks, with $$s \ge 2$$ , we show that each cycle of the classical block symmetric Gauss–Seidel (sGS) method exactly solves the associated quadratic programming (QP) problem but added with an extra proximal term of the form $$\frac{1}{2}\Vert {{\varvec{x}}}-{{\varvec{x}}}^k\Vert _\mathcal{T}^2$$ , where $$\mathcal{T}$$ is a symmetric positive semidefinite matrix related to the sGS decomposition of $$\mathcal{Q}$$ and $${{\varvec{x}}}^k$$ is the previous iterate. By leveraging on such a connection to optimization, we are able to extend the result (which we name as the block sGS decomposition theorem) for solving convex composite QP (CCQP) with an additional possibly nonsmooth term in $$x_1$$ , i.e., $$\min \{ p(x_1) + \frac{1}{2}\langle {{\varvec{x}}},\,\mathcal{Q}{{\varvec{x}}}\rangle -\langle {{\varvec{b}}},\,{{\varvec{x}}}\rangle \}$$ , where $$p(\cdot )$$ is a proper closed convex function. Based on the block sGS decomposition theorem, we extend the classical block sGS method to solve CCQP. In addition, our extended block sGS method has the flexibility of allowing for inexact computation in each step of the block sGS cycle. At the same time, we can also accelerate the inexact block sGS method to achieve an iteration complexity of $$O(1/k^2)$$ after performing k cycles. As a fundamental building block, the block sGS decomposition theorem has played a key role in various recently developed algorithms such as the inexact semiproximal ALM/ADMM for linearly constrained multi-block convex composite conic programming (CCCP), and the accelerated block coordinate descent method for multi-block CCCP.

Recent developments have clarified that some tools of Convex Geometry are closely related to separation theorems obtained in the field of Functional Inequalities. This phenomenon has motivated the investigation of convex structures induced by Chebyshev systems. The present note characterizes such a possible structure, completely describing its combinatorial invariants.

We study the following fractional Yamabe-type equation: { A s ⁢ u = u n + 2 ⁢ s n - 2 ⁢ s , u > 0 in ⁢ Ω , u = 0 on ⁢ ∂ ⁡ Ω , \left\{\begin{aligned} \displaystyle A_{s}u&\displaystyle=u^{\frac{n+2s}{n-2s}% },\\ \displaystyle u&\displaystyle>0&&\displaystyle\text{in }\Omega,\\ \displaystyle u&\displaystyle=0&&\displaystyle\text{on }\partial\Omega,\end{% aligned}\right. Here Ω is a regular bounded domain of ℝ n {\mathbb{R}^{n}} , n ≥ 2 {n\geq 2} , and A s {A_{s}} , s ∈ ( 0 , 1 ) {s\in(0,1)} , represents the fractional Laplacian operator ( - Δ ) s {(-\Delta)^{s}} in Ω with zero Dirichlet boundary condition. We investigate the effect of the topology of Ω on the existence of solutions. Our result can be seen as the fractional counterpart of the Bahri–Coron theorem [3].

In this paper, we derive a best possible k -wise extension to the well-known Snevily theorem on set systems (Snevily, 2003) which strengthens the well-known theorem by Furedi and Sudakov (2004). We also provide a conjecture which gives a common generalization to all existing non-modular L -intersection theorems.

We use suitably Page’s theorem to get effective results for interesting problems, by avoiding the ineffective Siegel’s theorem.

In this paper, we prove that some recent Rotfel’d theorem extensions are equivalent.

Locally L 0 -convex modules were introduced in Filipovic et al. (2009) [10] as the analytic basis for the study of conditional risk measures. Later, the algebra of conditional sets was introduced in Drapeau et al. (2016) [8] . In this paper we study locally L 0 -convex modules, and find exactly which subclass of locally L 0 -convex modules can be identified with the class of locally convex vector spaces within the context of conditional set theory. Second, we provide a version of the classical James' theorem of characterization of weak compactness for conditional Banach spaces. Finally, we state a conditional version of the Fatou and Lebesgue properties for conditional convex risk measures and, as application of the developed theory, we establish a version of the so-called Jouini–Schachermayer–Touzi theorem for robust representation of conditional convex risk measures defined on a L ∞ -type module.

In this paper, we introduce the notion of strictly convex n-normed space as a generalization strictly convex normed space and give few characterizations for such space. We also prove that every map...

This note shows that conservativeness of former results on stability analysis of uncertain systems based on the Kharitonov’s Theorem can be significantly reduced by means of convex structures which lead to linear matrix inequalities. The relationship between an interval-coefficient family of polynomials and interval matrices is exploited in order to obtain novel LMI tests whose advantages are twofold: feasibility domains (both in ordinary as well as D-stability) are increased while LMIs are efficiently solved via convex optimization techniques. Illustrative examples are provided.

Estimation of convex functions finds broad applications in science and engineering; however, the convex shape constraint complicates the asymptotic performance analysis of such estimators. This technical note is devoted to the minimax optimal estimation of univariate convex functions in a given Holder class. Particularly, a minimax lower bound in the supremum norm (or simply sup-norm) is established by constructing a novel family of piecewise quadratic convex functions in the Holder class. This result, along with a recent result on the minimax upper bound, gives rise to the optimal rate of convergence for the minimax sup-norm risk of convex functions with the Holder order between one and two. The present technical note provides the first rigorous justification of the optimal minimax risk for convex estimation on the entire interval of interest in the sup-norm.

We propose an alternative proof of Pellet’s theorem for matrix polynomials that, unlike existing proofs, does not rely on Rouche’s theorem. A similar proof is provided for the generalization to matrix polynomials of a result by Cauchy that can be considered as a limit case of Pellet’s theorem.

In this paper we are concerned with a Gordan-type theorem involving an arbitrary number of inequality functions. We not only state its validity under a weak convexity assumption on the functions, but also show it is an optimal result. We discuss generalizations of several recent results on nonlinear quadratic optimization, as well as a formula for the Fenchel conjugate of the supremum of a family of functions, in order to illustrate the applicability of that theorem of the alternative.

The aim of this paper is to explore Karamata’s mean value theorem. In the second section, we reformulate a mean theorem for the convex functions and prove some consequences. Also, we prove an integral version of Karamta theorem. Third section is reserved to the stability results. We establish some conditions for the stability of the intermediate point arising from Karamata, Godner and the integral mean theorem proved in the previous section.

Systems of convex inequalities in function spaces are considered. Solvability conditions are obtained in the form of a theorem of the alternative. We revisit some results from the literature where such theorems are incorrect. We present counterexamples concerning these results and by introducing some regularity conditions we obtain new theorems which are dedicated to solvability of systems of convex inequalities.

Summary This work gives new statement of the vertex solution theorem for exact bounds of the solution to linear interval equations and its novel proof by virtue of the convex set theory. The core idea of the theorem is to transform linear interval equations into a series of equivalent deterministic linear equations. Then, the important theorem is extended to find the upper and lower bounds of static displacements of structures with interval parameters. Following discussions about the computational efforts, a coupled framework based on vertex method (VM) is established, which allows us to solve many large-scale engineering problems with uncertainties using deterministic finite element software. Compared with the previous works, the contribution of this work is not only to obtain the exact bounds of static displacements but also lay the foundation for development of an easy-to-use interval finite element software. Numerical examples demonstrate the good accuracy of VM. Meanwhile, the implementation of VM and availability of the coupled framework are demonstrated by engineering example. Copyright © 2017 John Wiley & Sons, Ltd.

We proved some inequalities for concave functions. Those inequalities complemented a theorem obtained by Lee. Finally, we partially solved an open problem proposed by Zhang P.

A non-Euclidean analog of the generalized Darboux equation is considered. For the case where its solutions are radial functions of second variable we obtain a uniqueness result (Theorem 1) which deals with zero sets of these solutions. The example of the function in Theorem 2 of the paper shows that Theorem 1 cannot be essentially reinforced.

The aim of this paper is to generalize Minkowski’s theorem. This theorem is usually stated for a centrally symmetric convex body and a lattice both included in $$\mathbf {R}^n$$ . In some situations, one may replace the lattice by a more general set for which a notion of density exists. In this paper, we prove a Minkowski theorem for quasicrystals, which bounds from below the frequency of differences appearing in the quasicrystal and belonging to a centrally symmetric convex body. The last part of the paper is devoted to quite natural applications of this theorem to Diophantine approximation and to discretization of linear maps.

In this paper, another version of Schmidt’s subspace theorem is given and the corresponding Wirsing type result is also considered.

A minimax theorem is a theorem providing conditions which guarantee that the max–min inequality is also an equality. The first theorem in this sense is von Neumann’s minimax theorem, which was considered the starting point of game theory. Since then, several alternative generalizations of von Neumann’s original theorem have appeared in the literature. Variational inequality and minimax problems are of fundamental importance in modern non-linear analysis. They are widely applied in mechanics, differential equations, control theory, mathematical economics, game theory, and optimization. The purpose of this paper is first to establish a minimax theorem for mixed lower–upper semi-continuous functions in fuzzy metric spaces which extends the minimax theorems of many von Neumann types. As applications, we utilize this result to study the existence problems of solutions for abstract variational inequalities and quasi-variational inequalities in fuzzy metric spaces and to study the coincidence problems and saddle problems in fuzzy metric spaces.

In this paper, we prove the paragraded version of the Wedderburn–Artin theorem. Following the methods known from the abstract case, we first prove the density theorem and observe the matrix rings whose entries are from a paragraded ring. However, in order to arrive at the desired structure theorem, we introduce the notion of a Jacobson radical of a paragraded ring and prove some properties which are analogous to the abstract case. In the process, we study the faithful and irreducible paragraded modules over noncommutative paragraded rings and prove the paragraded version of the well-known Schur lemma.

Rouche’s Theorem is a standard topic in undergraduate complex analysis. It is usually covered near the end of the course with applications relating to pure mathematics only (e.g., using it to produce an alternate proof of the Fundamental Theorem of Algebra). The winding number provides a geometric interpretation relating to the conclusion of Rouche’s Theorem, but most undergraduate texts give no geometric insights that lead to an understanding of why Rouche’s Theorem holds. In addition, most texts do not inform students that a stronger version of the theorem exists. In this paper we present a simplified proof of the stronger version, which is a suitable topic for students to pursue as a short project, and provide a geometric argument for the weaker version. Finally, as a project for advanced students, we unpack a standard application of this theorem as used in control systems: the Nyquist stability criterion.

In this paper a nonsmooth mean value theorem in asplund spaces, under convexity, using the properties of limiting subdifferentials is established. We research on a kind of mean value theorem and prove that this theorem for set-valued mappings under convexity of domein in banach spaces. This theorem is use full to establish new results in convex analysis.

As one of the main results we prove that if f has Lagrange unique property then f is strictly convex or concave (we do not assume continuity of the derivative), Theorem 2.1. We give two different proofs of Theorem 2.1 (one mainly using Lagrange theorem and the other using Darboux theorem). In addition, we give a few characterizations of strictly convex curves, in Theorem 3.5. As an application of it, we give characterization of strictly convex planar curves, which have only tangents at every point, by injective of the Gauss map. Also without the differentiability hypothesis we get the characterization of strictly convex or concave functions by two points property, Theorem 4.2.

Applications of the generalization of Mazur-Orlicz theorem to concrete spaces are proved. Suitable moment problems are solved, as applications of extension theorems of linear operators with a convex and a concave constraint. In particular, a relationship between Mazur-Orlicz theorem and Markov moment problem is partially illustrated. In the end of this work, an application to the multidimensional Markov moment problem of an earlier extension result on a distanced subspace with respect to a bounded convex set is proved. Contrary to preceding results based on this theorem, now the solution is defined on a space of continuous functions vanishing at the origin. Most of the solutions are operator valued, respectively function valued.

We show that Kolmogorov's theorem on representations of continuous functions of n-variables by sums and superpositions of continuous functions of one variable is relevant in the context of neural networks. We give a version of this theorem with all of the one-variable functions approximated arbitrarily well by linear combinations of compositions of affine functions with some given sigmoidal function. We derive an upper estimate of the number of hidden units.

In this paper we survey a set of Brouwer fixed point theorem equivalents. These equivalents are divided into four loops related to (1) the Borsuk retraction theorem, (2) the Himmelberg fixed point theorem, (3) the Gale lemma and (4) the Nash equilibrium theorem.

This paper is concentrated on giving a module containment theorem for piecewise continuous almost periodic functions (pcap function for short). One first analyses the relationship between the translation number set and some Fourier exponents of a pcap function. And then, combining with Kronecker’s theorem, a module containment theorem for a pcap function is established for the first time. As an application, the module structure of a pcap solution for an impulsive differential equation is characterized. Some remarks and a corollary are given to show the advantage of the module containment theorem.

The author of the above mentioned paper found that pointwise ergodic theorem for bounded additive processes, cf. Theorem 7.11 needs an additional assumption. Including the assumption of approximability, we show that the ergodic theorem is correct. Since we have not found a proof for Poisson point processes being approximable, we withdraw Corollary 7.12 which stated the pointwise almost-everywhere convergence for those processes. A minor flaw in the proof of the abstract mean ergodic theorem (Theorem 5.7) is fixed as well.

In this paper, we study α ¯ -harmonic mappings with an integer exponent and obtain a representation theorem which determines the relation between α ¯ -harmonic mappings and Euclidean harmonic mappings. As two applications of this representation theorem, we obtain a counterexample of the Rado–Kneser–Choquet theorem for α ¯ -harmonic mappings and show that the Lipschitz continuity of an α ¯ -harmonic mapping with an integer exponent is determined by the Euclidean harmonic mapping with same boundary.

We prove constructively that every uniformly continuous convex function f : X -> R+ has positive infimum, where X is the convex hull of finitely many vectors. Using this result, we prove that a separating hyperplane theorem, the fundamental theorem of asset pricing, and Markov's principle are constructively equivalent. This is the first time that important theorems are classified into Markov's principle within constructive reverse mathematics. (C) 2016 Elsevier B.V. All rights reserved.

This paper gives, in an elementary way, estimates on derivatives and logarithmic derivatives of holomorphic functions and then, as an application, a brief proof of Picard's theorem.

In this paper, some new results, concerned with the geodesic convex hull and geodesic convex combination, are given on Hadamard manifolds. An S-KKM theorem on a Hadamard manifold is also given in order to generalize the KKM theorem. As applications, a Fan---Browder-type fixed point theorem and a fixed point theorem for the a new mapping class are proved on Hadamard manifolds.