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This paper investigates the elliptic localization for moving object problem from time delay (TD) and Doppler frequency shift (DFS) measurements, where the transmitter position and velocity are unknown. The transmitter is not perfectly time syncronized such that unknown offsets exist in the TD and DFS measurements. We propose to jointly estimate the object and transmitter positions and velocities and the offsets. Using the TD and DFS measurements from both the indirect and direct paths between the transmitter and the receivers, we formulate a non-convex weighted least squares (WLS) problem. Local convergence may occur when solving the non-convex WLS problem, implying that good estimate is not guaranteed. Thus, we relax the non-convex WLS problem into a convex semidefinite program by applying semidefinite relaxation (SDR). Moreover, we theoretically show that the performance can be improved by using multiple transmitters as compared to that using single transmitter, although more unknown parameters are introduced. We then extend the proposed SDR method to handle the multiple transmitters case. Finally, the mean square error analysis is provided to show that the proposed WLS method reaches the Cramer-Rao lower bound accuracy under small Gaussian noise condition. Simulation results validate the theoretical analysis and show the superior performance over the existing methods.

With the wide range application of machine learning in healthcare for helping humans drive crucial decisions, data privacy becomes an inevitable concern due to the utilization of sensitive data such as patients records and registers of a company. Thus, constructing a privacy preserving machine learning model while still maintaining high accuracy becomes a challenging problem. In this paper, we propose two differentially private algorithms, i.e., Output Perturbation with aGM (OPERA) and Gradient Perturbation with aGM (GRPUA) for empirical risk minimization, a useful method to obtain a globally optimal classifier, by leveraging the analytic Gaussian mechanism (aGM) to achieve privacy preservation of sensitive medical data in a healthcare system. We theoretically analyze and prove utility upper bounds of proposed algorithms and compare them with prior algorithms in the literature. The analyses show that in the high privacy regime, our proposed algorithms can achieve a tighter utility bound for both settings: strongly convex and non-strongly convex loss functions. Besides, we evaluate the proposed private algorithms on three benchmark datasets, i.e., Adult, BANK and IPUMS-BR. The simulation results demonstrate that our approaches can achieve higher accuracy and lower objective values compared with existing ones in all three datasets while providing differential privacy guarantees.

The Lyapunov equation for polytopic linear inclusions over the space of Minkowski functions of nonempty compact and convex sets that contain the origin as an interior point is studied. In particular, necessary and sufficient conditions for the characterization, existence and uniqueness of its fundamental solution are derived.

Uncertainties arising from renewable energy bring huge challenges in optimal power flow (OPF) analysis. Various chance constrained approaches are proposed to manage the uncertainties in OPF models. However, most existing approaches assume that the probability distributions of uncertainties are known \emph{a priori}, or consider chance constraint individually. This paper proposes a distributionally robust (DR) joint chance constrained OPF model, which ensures that all the operation constraints are satisfied with a given probability and does not require the assumption on specific probability distributions. An ambiguity set built on the first and second moments is used to model the uncertainties. An optimized Bonferroni approximation (OBA) is first introduced to decompose the DR joint chance constraint into DR individual chance constraints, the resulting OBA formulation is strongly non-convex. Different convex approximations are then proposed to formulate the OBA based DR individual chance constraints as tractable formulations. The proposed convex approximations can be easily extended to incorporate the structural information associated with uncertainties like unimodality and symmetry. Case studies demonstrate the effectiveness of the proposed convex approximation methods.

This brief addresses distributed continuous-time optimization problems with time-varying objective functions. The goal is for multiple agents to cooperatively minimize the sum of local time-varying objective functions with only local interaction and information while explicitly taking into account distributed adaptive gain design. Here, the optimal point is time varying and creates an optimal trajectory. First, for the unconstrained case, a distributed nonsmooth algorithm coupled with a state-dependent gain is proposed. It is shown that the interaction gain for each agent can be computed according to the variation of the Hessian and gradient information of the convex local objective functions so that the algorithm can solve the time-varying optimization problem without imposing a bound on any information about the local objective functions. Second, for the case where there exist common time-varying linear equality constraints, an extended algorithm is presented, where local Lagrangian functions are introduced to address the equality constraints. The asymptotic convergence of both algorithms to the optimal solution is proved. Numerical simulations are presented to illustrate the theoretical results. In addition, the one proposed algorithm is experimentally implemented and validated on a multi-Crazyflie platform.

This paper studies the distributed convex optimization problem with a common decision variable, a global inequality constraint, and local constraint sets over a time-varying multi-agent network, the objective function of which is a sum of agents' local convex cost functions. To solve such problem, a penalty-based distributed continuous-time subgradient algorithm with time-varying gain is developed for each agent to seek the saddle point of the penalty Lagrangian function. It is shown that an exact primal optimal solution can be obtained with certain assumption on time-varying gain. Moreover, the proposed algorithm adopts fixed-time projection scheme to ensure that for any initial state value, each local state estimate converges to its convex constraint set within fixed time. Finally, numerical examples are provided to show the effectiveness of the theoretical results.

The objective of the authors is to introduce the new class of convex fuzzy-interval-valued functions (convex-FIVFs), which is known as $ p $-convex fuzzy-interval-valued functions ($ p $-convex-FIVFs). Some of the basic properties of the proposed fuzzy-interval-valued functions are also studied. With the help of $ p $-convex FIVFs, we have presented some Hermite-Hadamard type inequalities ($ H-H $ type inequalities), where the integrands are FIVFs. Moreover, we have also proved the Hermite-Hadamard-Fejér type inequality ($ H-H $ Fejér type inequality) for $ p $-convex-FIVFs. To prove the validity of main results, we have provided some useful examples. We have also established some discrete form of Jense's type inequality and Schur's type inequality for $ p $-convex-FIVFs. The outcomes of this paper are generalizations and refinements of different results which are proved in literature. These results and different approaches may open new direction for fuzzy optimization problems, modeling, and interval-valued functions.

It is well-known fact that fuzzy interval-valued functions (F-I-V-Fs) are generalizations of interval-valued functions (I-V-Fs), and inclusion relation and fuzzy order relation on interval space and fuzzy space are two different concepts. Therefore, by using fuzzy order relation (FOR), we derive inequalities of Hermite-Hadamard (H·H) and Hermite-Hadamard Fejér (H·H Fejér) like for harmonically convex fuzzy interval-valued functions by applying fuzzy Riemann integrals. Moreover, we establish the relation between fuzzy integral inequalities and fuzzy products of harmonically convex fuzzy interval-valued functions. The outcomes of this study are generalizations of many known results which can be viewed as an application of a defined new version of inequalities.

In this paper, we study the sign-changing Kazdan-Warner's problem on two dimensional closed Riemannian manifold with negative Euler number $\chi(M)

In this paper, we present a new framework of bi-level unconstrained minimization for development of accelerated methods in Convex Programming. These methods use approximations of the high-order proximal points, which are solutions of some auxiliary parametric optimization problems. For computing these points, we can use different methods, and, in particular, the lower-order schemes. This opens a possibility for the latter methods to overpass traditional limits of the Complexity Theory. As an example, we obtain a new second-order method with the convergence rate $$O\left( k^{-4}\right) $$ O k - 4 , where k is the iteration counter. This rate is better than the maximal possible rate of convergence for this type of methods, as applied to functions with Lipschitz continuous Hessian. We also present new methods with the exact auxiliary search procedure, which have the rate of convergence $$O\left( k^{-(3p+1)/ 2}\right) $$ O k - ( 3 p + 1 ) / 2 , where $$p \ge 1$$ p ≥ 1 is the order of the proximal operator. The auxiliary problem at each iteration of these schemes is convex.

We study a general kind of partial information nonzero sum two-player stochastic differential games, where the state variable is governed by a stochastic differential equation and the control domain of each player can be non-convex. Moreover, the control variables of both players can enter the diffusion coefficients of the state equation. We establish Pontryagin’s maximum principle for open-loop Nash equilibria of the game. Then, a verification theorem is obtained for Nash equilibria when the control domain is convex. Finally, the theoretical results are applied to studying a linear-quadratic game.

We study the variance of the random variable that counts the number of lattice points in some shells generated by a special class of finite type domains in R d . The proof relies on estimates of the Fourier transform of indicator functions of convex domains.

Real life problems are entrenched in ambiguities. To deal with these ambiguities, stochastic functional analysis has emerged as one of the mathematical tools for solving these kinds of problems. Th...

Exhausters are families of compact, convex sets which provide minmax or maxmin representations of positively homogeneous functions and they are efficient tools for the study of nonsmooth functions (Demyanov in Optimization 45:13–29, 1999). Upper and lower exhausters of positively homogeneous functions are employed to describe optimality conditions in geometric terms and also to find directions of steepest descent or ascent. Since an upper/lower exhauster may contain finitely or infinitely many compact convex sets, the problem of minimality and reduction of exhausters naturally arise. There are several approaches to reduce exhausters (Abbasov in J Glob Optim 74(4):737–751, 2019; Grzybowski et al. in J Glob Optim 46(4):589–601, 2010; Kucuk et al. in J Optim Theory Appl 165:693–707, 2015; Roshchina in J Optim Theory Appl 136:261–273, 2008; J Convex Anal 15(4):859–868, 2008). In this study, in the sense of inclusion-minimality, some reduction techniques for upper exhausters of positively homogeneous functions defined from $$\mathbb {R}^2$$ to $$\mathbb {R}$$ is proposed by means of a representation of support functions. These techniques have concrete geometric meanings and they form a basis for a necessary and sufficient condition for inclusion-minimality of exhausters. Some examples are presented to illustrate each reduction technique.

In this article we study some geometric properties of proximally smooth sets. First, we introduce a modification of the metric projection and prove its existence. Then we provide an algorithm for constructing a rectifiable curve between two sufficiently close points of a proximally smooth set in a uniformly convex and uniformly smooth Banach space, with the moduli of smoothness and convexity of power type. Our algorithm returns a reasonably short curve between two sufficiently close points of a proximally smooth set, is iterative and uses our modification of the metric projection. We estimate the length of the constructed curve and its deviation from the segment with the same endpoints. These estimates coincide up to a constant factor with those for the geodesics in a proximally smooth set in a Hilbert space.

This article is concerned with the infinite horizon stochastic cooperative linear-quadratic (LQ) dynamic difference game in both the regular and the indefinite cases. Firstly, due to the constraints imposed on the weighting matrices and the linearity of the dynamic system, the costs are shown to be convex spontaneously for the regular stochastic cooperative LQ difference game, which yields the equivalence between the minimization of the weighted sum of costs and the Pareto optimal control. Secondly, the Pareto optimal control is derived for the regular game on the ground of the solution to the weighted algebraic Riccati equation (WARE) under exact observability, and then Pareto solutions are identified via the optimal feedback gain matrices and the solution to the weighted algebraic Lyapunov equation (WALE). Moreover, a new criterion which is also necessary and sufficient is developed to guarantee the costs to be convex for the indefinite case, and the Pareto optimality is investigated based on the solutions to the weighted generalized algebraic Riccati equation (WGARE) and the weighted generalized algebraic Lyapunov equation (WGALE) combining with the semidefinite programming (SDP). Finally, the fishery management game in the economy is presented to illustrate the obtained results.

In this paper, a Hessian type system is studied. After converting the existence of an entire solution to the existence of a fixed point of a continuous mapping, the existence of entirek-convex radial solutions is established by the monotone iterative method. Moreover, a nonexistence result is also obtained.

We prove unexpected decay of the L2-distance from the solution u(t) of a hyperbolic scalar conservation law, to some convex, flow-invariant target sets.

We compare the solutions of two Poisson problems in a spherical shell with Robin boundary conditions, one with given data, and one where the data has been cap symmetrized. When the Robin parameters are nonnegative, we show that the solution to the symmetrized problem has larger (increasing) convex means. We prove similar results on balls. We also prove a comparison principle on generalized cylinders with mixed boundary conditions (Neumann and Robin)., Improved notation, new mixed results added, typos corrected

We consider a class of generalized DC (difference-of-convex functions) programming, which refers to the problem of minimizing the sum of two convex (possibly nonsmooth) functions minus one smooth convex part. To efficiently exploit the structure of the problem under consideration, in this paper, we shall introduce a unified Douglas–Rachford method in Hilbert space. As an interesting byproduct of the unified framework, we can easily show that our proposed algorithm is able to deal with convex composite optimization models. Due to the nonconvexity of DC programming, we prove that the proposed method is convergent to a critical point of the problem under some assumptions. Finally, we demonstrate numerically that our proposed algorithm performs better than the state-of-the-art DC algorithm and alternating direction method of multipliers (ADMM) for DC regularized sparse recovery problems.

Detonation reflections over cylindrical convex-straight coupled surfaces were numerically analyzed using the density-based compressible–reactive solver DCRFoam developed on the framework of OpenFOAM-V7. The two-dimensional reactive Euler equations were adopted to calculate the reflection dynamics, considering a detailed chemical mechanism. Effects of the global wedge angle (ranging from 20° to 50°) and the radius of the former convex section (ranging from 25 mm to 125 mm) on the detonation reflections were discussed. It was founded that the original regular reflection could transform into the Mach reflection on the former cylindrical convex section for coupled surfaces with global wedge angles smaller than 50°. The peak pressures of the reflection points in the configurations of the regular reflection were much larger than those in the Mach reflection, and the increase in the wedge angle led to the increase of the average peak pressure of the reflection points in the Mach reflection on the following straight section. For a coupled surface with a global wedge angle of 50°, no Mach reflection configurations were established on the former cylindrical convex section. The existence of the convex section was proven to delay the establishment of the Mach reflection on the following straight section. For coupled surfaces with the same global wedge angles, the transition angles from the regular reflection to the Mach reflection increased with the increase in the radius.

The paper is devoted to the behavior of volume ratios, the modified Banach–Mazur distance, and the vertex index for sums of convex bodies. It is shown that sup d ( A ⊕ K , B ⊕ L ) ≥ sup ∂ ( A ⊕ K , B ⊕ L ) ≥ c ⋅ n 1 − k + k ′ 2 n , \begin{equation*} \sup d (\mathrm {A}\oplus \mathrm {K},\mathrm {B}\oplus \mathrm {L}) \geq \sup \partial (\mathrm {A}\oplus \mathrm {K},\mathrm {B}\oplus \mathrm {L}) \geq c \cdot n^{1-\frac {k+k’}{2n}}, \end{equation*} if K ⊂ R n \mathrm {K}\subset \mathbb {R}^n and L ⊂ R k \mathrm {L}\subset \mathbb {R}^k are convex and symmetric (the supremum is taken over all symmetric convex bodies A ⊂ R n − k \mathrm {A}\subset \mathbb {R}^{n-k} and B ⊂ R n − k ′ ) \mathrm {B}\subset \mathbb {R}^{n-k’}) . Furthermore, some examples are discussed that show that the available extimates of the vertex index in terms of the volume ratio are not sharp.

In this paper, we propose an approach to characterizing $${\epsilon} $$ -solution sets of convex programs with a given $${\epsilon} >0$$ . The results are divided into two parts. The first one is devoted to establishing the expressions of $${\epsilon} $$ -solution sets of a class of convex infinite programs. The representation is given based on the study of relationships among the following three sets: the set of Lagrange multipliers corresponding to a given $${\epsilon} $$ -solution, the set of $${\epsilon} $$ -solutions of the dual problem corresponding, and the set of $${\epsilon} $$ -Kuhn–Tucker vectors associated with the problem in consideration. The second one is devoted to some special cases: the $${\epsilon} $$ -solution sets of convex programs that have set constraints and the almost $${\epsilon} $$ -solution sets of convex programs that have finite convex constraints. Several examples are given.

Bounds are obtained for the efficiency or mean to peak ratio $E(\Omega)$ for the first Dirichlet eigenfunction (positive) for open, connected sets $\Omega$ with finite measure in Euclidean space $\R^m$. It is shown that (i) localisation implies vanishing efficiency, (ii) a vanishing upper bound for the efficiency implies localisation, (iii) localisation occurs for the first Dirichlet eigenfunctions for a wide class of elongating bounded, open, convex and planar sets, (iv) if $\Omega_n$ is any quadrilateral with perpendicular diagonals of lengths $1$ and $n$ respectively, then the sequence of first Dirichlet eigenfunctions localises, and $E(\Omega_n)=O\big(n^{-2/3}\log n\big)$. This disproves some claims in the literature. A key technical tool is the Feynman-Kac formula., Comment: 18 pages

In this paper we introduce point-ellipse configurations and point-conic configurations. We study some of their basic properties, illustrated by properly chosen examples, and describe two interesting infinite families of balanced point-ellipse, respectively point-conic 6-configurations. The construction of the first family is based on Carnot’s theorem, whilst the construction of the second family is based on the Cartesian product of two regular polygons. Finally, we describe a point-conic configuration based on the regular 24-cell.

In this paper, the problem of asynchronous control for a class of discrete-time switched systems is investigated under mode-dependent integrated dwell time (MDIDT) switching. By constructing a time-dependent convex function, a multiple convex Lyapunov function (MCLF) is firstly proposed for the asynchronous control of the switched systems. Under the MDIDT switching strategy, the matching interval is divided reasonably, and the convex function combination is constructed on the partitioned interval. Under asynchronous switching, the Lyapunov function is continuous when the subsystem mode is switched, but discrete when the controller mode is changed. Then, the increase of the Lyapunov function in the mismatched interval will be offset by the attenuation in the matched interval. In light of these merits, the stability results of the system are deduced, and the asynchronous controller is devised to guarantee the globally uniformly exponentially stability of the closed-loop system. Comparing with the traditional asynchronous control methods, the proposed method has less conservative results and larger stability regions. Finally, a numerical example and an application example are demonstrated to verify the validity and superiority of the asynchronous control scheme.

A convex polytope is the convex hull of a finite set of points in the Euclidean space ℝ n . By preserving the adjacency-incidence relation between vertices of a polytope, its structural graph is constructed. A graph is called Hamilton-connected if there exists at least one Hamiltonian path between any of its two vertices. The detour index is defined to be the sum of the lengths of longest distances, i.e., detours between vertices in a graph. Hamiltonian and Hamilton-connected graphs have diverse applications in computer science and electrical engineering, whereas the detour index has important applications in chemistry. Checking whether a graph is Hamilton-connected and computing the detour index of an arbitrary graph are both NP-complete problems. In this paper, we study these problems simultaneously for certain families of convex polytopes. We construct two infinite families of Hamilton-connected convex polytopes. Hamilton-connectivity is shown by constructing Hamiltonian paths between any pair of vertices. We then use the Hamilton-connectivity to compute the detour index of these families. A family of non-Hamilton-connected convex polytopes has also been constructed to show that not all convex polytope families are Hamilton-connected.

The geometry conjecture, which was posed nearly a quarter of a century ago, states that the fixed point set of the composition of projectors onto nonempty closed convex sets in Hilbert space is actually equal to the intersection of certain translations of the underlying sets. In this paper, we provide a complete resolution of the geometry conjecture. Our proof relies on monotone operator theory. We revisit previously known results and provide various illustrative examples. Comments on the numerical computation of the quantities involved are also presented.

We would like to congratulate the authors for their contribution to this longstanding open problem in mathematical statistics. Their clever implementation of MCMC to obtain simplex-based Dempster–S...

We are very grateful to all commenters for their stimulating remarks, questions, as well as useful pointers to the literature which span a wide range of statistical methods over decades of research...

The reflection of a converging cylindrical shock over a double curved wedge was investigated experimentally and numerically. The double curved wedge was specially designed to ensure that the wedge angle only changes at the corner during the cylindrical shock reflection. Seven different reflection processes existing in the planar case were reproduced in the converging case. The sudden variation of the wedge angle alters the strength of the disturbance propagating along the converging shock. Depending upon the type of the flow-induced pressure waves, which dominate the type of the wave behind the reflection over the second wedge, the propagation of the disturbance is either promoted or restrained. Comparison of the lengths of the disturbed shock front between the single wedge reflection and the double wedge reflection indicates that the history of the reflection over the first wedge would affect the reflection over the second wedge. Provided that the Mach reflection over the second wedge is fully developed, it approaches the Mach reflection of an identical shock over a single wedge with the same wedge angle as the second wedge. Relative to the planar case, the convex curved surface in the converging case promotes the disturbance propagation and increases the trajectory angle of the triple point.

This paper concerns the assessment of two methods for convex relaxation of the short-term hydrothermal scheduling problem. The problem is originally formulated as a mixed integer programming problem, and then approximated using both Lagrangian and Linear relaxation. The two relaxation methods are quantitatively compared using a realistic data description of the Northern European power system, considering a set of representative days. We find that the Lagrangian relaxation approximates system operational costs in the range 55-81% closer to the mixed integer programming problem solution than the Linear relaxation. We show how these cost gaps vary with season and climatic conditions. Conversely, the differences in both marginal cost of electricity and reserve capacity provided by the Lagrangian and Linear relaxation are muted.

In this paper, we investigate $$L^{p}$$ estimates ( $$1\leq p\leq+\infty$$ ) and $$f$$ -Holder estimates for the Cauchy–Riemann equation in a class of convex domains of finite or infinite type with piecewise smooth boundary in $$\mathbb{C}^{2}$$ .

In this paper, we consider a nonlinear Schrodinger equation involving the fractional Laplacian with Dirichlet condition: $$\begin{aligned} {\left\{ \begin{array}{ll} (-\Delta )^{\frac{\alpha }{2}}u+A(x)u=f(x,u,\nabla u) \ \text{ in } \ \Omega ,\\ u>0, \ \text{ in }\ \Omega ; \ u\equiv 0, \ \text{ in }\ \mathbb R^n\backslash \Omega , \end{array}\right. } \end{aligned}$$ where $$\Omega $$ is a domain (bounded or unbounded) in $$\mathbb R^n$$ which is convex in $$x_1$$ -direction. By using some ideas of maximum principle and the direct moving plane method, we prove that the solutions are strictly increasing in $$x_1$$ -direction in the left half domain of $$\Omega $$ . Symmetry of some solutions are also proved. Meanwhile, we obtain a Liouville type theorem on the half space $$\mathbb R^n_+$$ .

Every non-convex pair $(C, D)$ ( C , D ) may not have proximal normal structure even in a Hilbert space. In this article, we use cyclic relatively nonexpansive maps with respect to orbits to show the presence of best proximity points in $C\cup D$ C ∪ D , where $C\cup D$ C ∪ D is a cyclic T-regular set and $(C, D)$ ( C , D ) is a non-empty, non-convex proximal pair in a real Hilbert space. Moreover, we show the presence of best proximity points and fixed points for non-cyclic relatively nonexpansive maps with respect to orbits defined on $C\cup D$ C ∪ D , where C and D are T-regular sets in a uniformly convex Banach space satisfying $T(C)\subseteq C$ T ( C ) ⊆ C , $T(D)\subseteq D$ T ( D ) ⊆ D wherein the convergence of Kranoselskii’s iteration process is also discussed.

In this paper, we propose and analyze a variant of the proximal point method for obtaining weakly efficient solutions of convex vector optimization problems in real Hilbert spaces, with respect to a partial order induced by a closed, convex and pointed cone with nonempty interior. The proposed method is a hybrid scheme that combines proximal point type iterations and projections onto some special halfspaces in order to achieve the strong convergence to a weakly efficient solution. To the best of our knowledge, this is the first time that proximal point type method with strong convergence has been considered in the literature for solving vector/multiobjective optimization problems in infinite dimensional Hilbert spaces.

This paper deals with (1, 1; r)-convexity of sequences. First, we prove several results on the sets of (1, 1; r)-convex sequences for various values of r. Then we show that the Cesaro matrix does not preserve (1, 1; r)-convexity of sequences for any $$r\ge 2$$ .

This paper studies a steelmaking-continuous casting scheduling problem with different processing routes. We model this problem as a mixed-integer nonlinear programming problem. Next, Lagrangian rel...

In this study, we discuss and develop a distributionally robust joint chance-constrained optimization model and apply it for the shortest path problem under resource uncertainty. In sch a case, robust chance constraints are approximated by constraints that can be reformulated using convex programming. Since the issue we are discussing here is of the multi-resource type, the resource related to cost is deterministic; however, we consider a robust set for other resources where covariance and mean are known. Thus, the chance-constrained problem can be expressed in terms of a cone constraint. In addition, since our problem is joint chance-constrained optimization, we can use Bonferroni approximation to divide the problem into L separate problems in order to build convex approximations of distributionally robust joint chance constraints. Finally, numerical results are presented to illustrate the rigidity of the bounds and the value of the distributionally robust approach.

Hardy and Rellich type inequalities with an additional term are proved for compactly supported smooth functions on open subsets of the Euclidean space. We obtain one-dimensional Hardy type inequalities and their multidimensional analogues in convex domains with the finite inradius. We use Bessel functions and the Lamb constant. The statements proved are a generalization for the case of arbitrary p ⩾ 2 of the corresponding inequality proved by F. G. Avkhadiev, K.-J. Wirths (2011) for p = 2. Also we establish Rellich type inequalities on arbitrary domains, regular sets, on domains with θ-cone condition and on convex domains.

In this paper, we first employ the subdifferential closedness condition and Guignard’s constraint qualification to present “dual cone characterizations” of the constraint set $$ \varOmega $$ with infinite nonconvex inequality constraints, where the constraint functions are Frechet differentiable that are not necessarily convex. We next provide sufficient conditions for which the “strong conical hull intersection property” (strong CHIP) holds, and moreover, we establish necessary and sufficient conditions for characterizing “perturbation property” of the best approximation to any $$x \in {\mathcal {H}}$$ from the convex set $$ \tilde{\varOmega }:=C \cap \varOmega $$ by using the strong CHIP of $$\lbrace C,\varOmega \rbrace ,$$ where C is a non-empty closed convex set in the Hilbert space $${\mathcal {H}}.$$ Finally, we derive the “Lagrange multiplier characterizations” of constrained best approximation under the subdifferential closedness condition and Guignard’s constraint qualification. Several illustrative examples are presented to clarify our results.

In this paper, the asymptotic stability of Port-Hamiltonian (PH) systems with constant inputs is studied. Constant inputs are useful for stabilizing systems at their nonzero equilibria and can be realized by step signals. To achieve this goal, two methods based on integral action and comparison principle are presented in this paper. These methods change the convex Hamiltonian function and the restricted damping matrix of the previous results into a Hamiltonian function with a local minimum and a positive semidefinite matrix, respectively. Due to common conditions of Hamiltonian function and damping matrix, the proposed method asymptotically stabilizes more classes of PH systems with constant inputs than the existing methods. Finally, the validity and advantages of the presented methods are shown in an example.

We show that 2D Navier–Stokes and Euler flows with localised vorticity always feature regular structures in the far field. The level lines of each component of the velocity, at large distances, tend to have the symmetries of a regular polygon: a digon if the total circulation is non-zero; a square for flows with zero total circulation and non-integrable velocity; an hexagon for flows with integrable velocity and, exceptionally, a polygon with more than six sides.

Let G(V, E) be a simple, connected and undirected graph. A dominating set $$S \subseteq V$$ is called a 2-secure dominating set (2-SDS) in G, if for each pair of distinct vertices $$v_1,v_2 \in V$$ there exists a pair of distinct vertices $$u_1,u_2 \in S$$ such that $$u_1 \in N[v_1]$$ , $$u_2 \in N[v_2]$$ and $$(S {\setminus } \{u_1,u_2\}) \cup \{v_1,v_2 \}$$ is a dominating set in G. The size of a minimum 2-SDS in G is said to be 2-secure domination number denoted by $$\gamma _{2s}(G)$$ . The 2-SDM problem is to check if an input graph G has a 2-SDS S, with $$ \vert S \vert \le k$$ , where $$ k \in \mathbb {Z}^+ $$ . It is proved that for bipartite graphs 2-SDM is NP-complete. In this paper, we prove that the 2-SDM problem is NP-complete for planar graphs and doubly chordal graphs, a subclass of chordal graphs. We reinforce the existing NP-complete result for bipartite graphs, by proving 2-SDM is NP-complete for some subclasses of bipartite graphs specifically, comb convex bipartite and star convex bipartite graphs. We prove that this problem is linear time solvable for bounded tree-width graphs. We also show that the 2-SDM is W[2]-hard even for split graphs. The M2SDS problem is to find a 2-SDS of minimum size in the given graph. We give a $$ \varDelta +1 $$ -approximation algorithm for M2SDS, where $$ \varDelta $$ is the maximum degree of the given graph and prove that M2SDS cannot be approximated within $$ (1 - \epsilon ) \ln (\vert V \vert ) $$ for any $$ \epsilon > 0 $$ unless $$ NP \subseteq DTIME(\vert V \vert ^{ O(\log \log \vert V \vert )}) $$ . Finally, we prove that the M2SDS is APX-complete for graphs with $$\varDelta =4.$$

In this paper, we consider the weak Galerkin finite element approximations of second order linear parabolic problems in two dimensional convex polygonal domains under the low regularities of the solutions. Optimal order error estimates in L 2 ( L 2 ) and L 2 ( H 1 ) norms are shown to hold for both the spatially discrete continuous time and the discrete time weak Galerkin finite element schemes, which allow using the discontinuous piecewise polynomials on finite element partitions with the arbitrary shape of polygons with certain shape regularity. The fully discrete scheme is based on first order in time Euler method. We have derived O ( h r + 1 ) in L 2 ( L 2 ) norm and O ( h r ) in L 2 ( H 1 ) norm when the exact solution u ∈ L 2 ( 0 , T ; H r + 1 ( Ω ) ) ∩ H 1 ( 0 , T ; H r − 1 ( Ω ) ) , for some r ≥ 1 . Numerical experiments are reported for several test cases to justify our theoretical convergence results.

International audience; We prove the well-posedness of a dynamical perfect plasticity model under general assumptions on the stress constraint set and on the reference configuration. The problem is studied by combining both calculus of variations and hyperbolic methods. The hyperbolic point of view enables one to derive a class of dissipative boundary conditions, somehow intermediate between homogeneous Dirichlet and Neumann ones. By using variational methods, we show the existence and uniqueness of solutions. Then we establish the equivalence between the original variational solutions and generalized entropic-dissipative ones, derived from a weak hyperbolic formulation for initial-boundary value Friedrichs' systems with convex constraints.