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In this paper, we have analysed the influence of viscoelastic and frictional damping on the decay rate of solutions for a Kirchhoff-type viscoelastic wave equation with a distributed delay acting on nonlinear internal damping. Taking the relaxation function of a fairly large class and using the method of energy in which we introduce an adapted Lyapunov functional and by exploiting certain properties of convex functions, under certain assumptions on the constants of system, we obtain the optimal decay rate of energy in the sense that it is compatible with the decay rate of the relaxation function. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the notion of strongly generalized convex functions which is called as strongly η-convex stochastic processes. We prove the Hermite-Hadamard, Ostrowski type inequality, and obtain some important inequalities for above processes. Some previous results are special cases of the results obtained in this paper. [ABSTRACT FROM AUTHOR]

It is undoubtedly that mathematical modelling and optimisation play a key role in the supply chain and the production management (SCPM). In this paper, we provide a survey on DC (Difference of Convex function) programming and DCA (DC Algorithm), a state-of-the-art optimisation approach for challenging problems in SCPM. DC programming and DCA constitute the backbone of non-convex programming and global optimisation. Whilst DC programming and DCA were widely and successfully investigated in many areas, it seems that they were not so much popular in the community of SCPM. There is therefore a need to further develop this efficient and scalable approach for SCPM applications, especially for large-scale problems in the context of Big data. For such purpose, this paper aims to present benchmark models and state-of-the-art DCA-based methods for solving challenging problems in SCPM systems. We prove that all the benchmark classes of optimisation models appeared in SCPM systems can be formulated/reformulated as a DC program and show how to solve these classes of problems by DCA-based algorithms. We offer the community of researchers in SCPM efficient algorithms in a unified DC programming framework to tackle various applications such as supply chain design, scheduling, multi-stage production/inventory system, vehicle routing, ... [ABSTRACT FROM AUTHOR]

One of the fundamental inequalities which is used in many inequities is Jensen inequality. In fact, it is a base of some inequality such as the arithmetic mean, harmonic mean inequality also in inequality with respect to entropies and information theory. The purpose of this research paper is to give a new interesting refinement of Jensen inequality for two particular finite sequences by using uniformly convex function. Also, we give some applications of this in information theory. [ABSTRACT FROM AUTHOR]

In this paper, we consider classes of approximate Minty and Stampacchia type vector variational inequalities using Clarke subdifferential on Hadamard manifolds and a class of nonsmooth multiobjective optimization problems. We investigate the relationship between the solution of these approximate vector variational inequalities and the solution of nonsmooth multiobjective optimization problems involving geodesic approximately convex functions. The results presented in this paper extend and generalize some existing results in the literature. [ABSTRACT FROM AUTHOR]

In this paper, the variation of functions has been defined, whose domain is a convex and compact set in the plane. Furthermore, in addition to presenting properties that satisfy this variation, the vector space formed by functions with finite variation is studied, demonstrating that it is a Banach space and its elements can be expressed as the difference of non-decreasing functions. [ABSTRACT FROM AUTHOR]

Given a set H of functions defined on a set X, á function f : X ↦ R ¯ is called abstract H -convex if it is the upper envelope of its H -minorants, i.e. such its minorants which belong to the set H ; and f is called regularly abstract H -convex if it is the upper envelope of its maximal (with respect to the pointwise ordering) H -minorants. In the paper we first present the basic notions of (regular) H -convexity for the case when H is an abstract set of functions. For this abstract case a general sufficient condition based on Zorn's lemma for a H -convex function to be regularly H -convex is formulated. The goal of the paper is to study the particular class of regularly H -convex functions, when H is the set L C ˆ (X , R) of real-valued Lipschitz continuous classically concave functions defined on a real normed space X. For an extended-real-valued function f : X ↦ R ¯ to be L C ˆ -convex it is necessary and sufficient that f be lower semicontinuous and bounded from below by a Lipschitz continuous function; moreover, each L C ˆ -convex function is regularly L C ˆ -convex as well. We focus on L C ˆ -subdifferentiability of functions at a given point. We prove that the set of points at which an L C ˆ -convex function is L C ˆ -subdifferentiable is dense in its effective domain. This result extends the well-known classical Brøndsted-Rockafellar theorem on the existence of the subdifferential for convex lower semicontinuous functions to the more wide class of lower semicontinuous functions. Using the subset L C ˆ θ of the set L C ˆ consisting of such Lipschitz continuous concave functions that vanish at the origin we introduce the notions of L C ˆ θ -subgradient and L C ˆ θ -subdifferential of a function at a point which generalize the corresponding notions of the classical convex analysis. Some properties and simple calculus rules for L C ˆ θ -subdifferentials as well as L C ˆ θ -subdifferential conditions for global extremum points are established. Symmetric notions of abstract L C ˇ -concavity and L C ˇ -superdifferentiability of functions where L C ˇ := L C ˇ (X , R) is the set of Lipschitz continuous convex functions are also considered. [ABSTRACT FROM AUTHOR]

This paper aims to establish a general stability result for a one-dimensional linear swelling porous-elastic system with infinite memory, irrespective of the wave speeds of the system. The proof is based on the multiplier method and some properties of convex functions. The kernel in our memory term is more general and of a broader class. Our output extends and improves some of the available results on swelling porous media in the literature. [ABSTRACT FROM AUTHOR]

In this paper, the problem of designing static output feedback (SOF) H 2 negative imaginary controller has been studied. Because the constraints brought by negative imaginary (NI) property and H 2 performance are a set of bilinear matrix inequalities (BMIs), designing an SOF NI controller with H 2 performance is a non-trivial problem. To overcome the difficulty of solving BMIs, a linearisation-based method is proposed. First, a necessary and sufficient condition is established, where the constraints of NI property and H 2 performance are reformulated as similar forms. Second, inspired by the semidefinite programming, the derived condition is converted to the difference between two convex functions. Then a linearised iterative algorithm with initialisation process is provided to compute the desired SOF H 2 NI controller. Finally, two numerical examples are presented to illustrate the proposed results. [ABSTRACT FROM AUTHOR]

In recent years, the study of Hankel determinants for various subclasses of normalised univalent functions f ∈ S given by f (z) = z + ∑ n = 2 ∞ a n z n for D = { z ∈ C : | z | < 1 } has produced many interesting results. The main focus of interest has been estimating the second Hankel determinant of the form H 2 , 2 (f) = a 2 a 4 − a 3 2 . A non-sharp bound for H 2 , 2 (f) when f ∈ K (α) , α ∈ [ 0 , 1) consisting of convex functions of order α was found by Krishna and Ramreddy (Hankel determinant for starlike and convex functions of order alpha. Tbil Math J. 2012;5:65–76), and later improved by Thomas et al. (Univalent functions: a primer. Berlin: De Gruyter; 2018). In this paper, we give the sharp result. Moreover, we obtain sharp results for H 2 , 2 (f − 1) for the inverse functions f − 1 when f ∈ K (α) , and when f ∈ S ∗ (α) , the class of starlike functions of order α. Thus, the results in this paper complete the set of problems for the second Hankel determinants of f and f − 1 for the classes S ∗ (α) , K (α) , S β ∗ and K β , where S β ∗ and K β are, respectively, the classes of strongly starlike, and strongly convex functions of order β. [ABSTRACT FROM AUTHOR]

Metric regularity is widely concerned since its important applications in optimization and control theory. For promoting the application of metric regularity, it is valuable to study the estimation of the neighborhood which makes the regularity hold. However, it seems that no result has been established about this issue. This paper investigates the estimation of the neighborhood of metric regularity for quadratic functions. The main result gives the expression of the neighborhood of metric regularity for a kind of convex quadratic functions. [ABSTRACT FROM AUTHOR]

In this paper we present two Fenchel-type dual problems for a DC (difference of convex functions) optimization primal one. They have been built by means of the c-conjugation scheme, a pattern of conjugation which has been shown to be suitable for evenly convex functions. We study characterizations of weak, strong and stable strong duality for both pairs of primal–dual problems. We also give conditions which relate the existence of strong and stable strong duality for both pairs. [ABSTRACT FROM AUTHOR]

In this paper, a numerical approximation method is developed to find approximate solutions to a class of constrained multi-objective optimization problems. All the functions of the problem are not necessarily convex functions. At each iteration of the method, a particular type of subproblem is solved using the trust region technique, and the step is evaluated using the notions of actual reduction and predicted reduction. A non-differentiable $ l_{\infty } $ l∞ penalty function restricts the constraint violations. An adaptive BFGS update formula is introduced. Global convergence of the proposed algorithm is established under the Mangasarian-Fromovitz constraint qualification and some mild assumptions. Furthermore, it is justified that the proposed algorithm displays a super-linear convergence rate. Numerical results are provided to show the efficiency of the algorithm in the quality of the approximated Pareto front. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of minimizing the function $ f(x)=g_1(x)+g_2(x)-h(x) $ f(x)=g1(x)+g2(x)−h(x) over $ \mathbb {R}^n $ Rn, where $ g_1 $ g1 is a proper and lower semicontinuous function, $ g_2 $ g2 is continuously differentiable with a Hölder continuous gradient and h is a convex function that may be nondifferentiable. This problem has important practical applications but is challenging to solve due to the presence of nonconvexities and nonsmoothness. To address this issue, we propose an algorithm based on a proximal gradient method that uses a quadratic approximation of the function $ g_2 $ g2 and a nonconvex regularization term. We show that the number of iterations required to reach our stopping criterion is $ \mathcal {O}(\max \{\epsilon ^{-\frac {\beta +1}{\beta }},\eta ^\frac {2}{\beta } \epsilon ^{-\frac {2(\beta +1)}{\beta }}\}) $ O(max{ϵ−β+1β,η2βϵ−2(β+1)β}). Our approach offers a promising strategy for solving this challenging optimization problem and has potential applications in various fields. Numerical examples are provided to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, we consider a viscoelastic equation with nonlinear feedback localized on a part of the boundary and in the presence of infinite-memory term. With imposing a more general condition on the relaxation function, we establish a more general stability result that generalizes and improves many earlier results in the literature. Our results are obtained without imposing any restrictive growth assumption on the damping term and without using any assumption on the boundedness of initial data used in many earlier papers in the literature. [ABSTRACT FROM AUTHOR]

In this paper, two modified spectral conjugate gradient methods which satisfy sufficient descent property are developed for unconstrained optimization problems. For uniformly convex problems, the first modified spectral type of conjugate gradient algorithm is proposed under the Wolfe line search rule. Moreover, the search direction of the modified spectral conjugate gradient method is sufficiently descent for uniformly convex functions. Furthermore, according to the Dai-Liao's conjugate condition, the second spectral type of conjugate gradient algorithm can generate some sufficient decent direction at each iteration for general functions. Therefore, the second method could be considered as a modification version of the Dai-Liao's algorithm. Under the suitable conditions, the proposed algorithms are globally convergent for uniformly convex functions and general functions. The numerical results show that the approaches presented in this paper are feasible and efficient. [ABSTRACT FROM AUTHOR]

In this paper, we study the optimization problem (PWE) of minimizing a convex function over the set of weakly efficient solutions of a convex multiobjective problem. This is done by using the fact that each lower semicontinuous convex function is an upper envelope of its affine minorants together with a generalized cutting plane method. We give necessary conditions for optimal solutions of the problem (PWE). Moreover, a novel algorithm for solving the problem (PWE) together with numerical results are presented. We also prove that the proposed algorithm terminates after a finite numbers of iterations, and the algorithm is coded in MATLAB language and evaluated by numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, we develop a regularized Fenchel dual gradient method (RFDGM), which allows nodes in a time-varying undirected network to find a common decision, in a fully distributed fashion, for minimizing the sum of their local objective functions subject to their local constraints. Different from most existing distributed optimization algorithms that also cope with time-varying networks, RFDGM is able to handle problems with general convex objective functions and distinct local constraints, and still has non-asymptotic convergence results. Specifically, under a standard network connectivity condition, we show that RFDGM is guaranteed to reach ϵ-accuracy in both optimality and feasibility within O (1 ϵ 2 ln ⁡ 1 ϵ) iterations. Such iteration complexity can be improved to O (1 ϵ ln ⁡ 1 ϵ) if the local objective functions are strongly convex but not necessarily differentiable. Finally, simulation results demonstrate the competence of RFDGM in practice. [ABSTRACT FROM AUTHOR]

In this article, the time to calculate the optimal height of the UAV is investigated as an important factor in determining the total flight time, energy consumption and total delay. In particular, in this paper, the calculation of the optimal height of the UAV, reducing the time of calculating the optimal height, reducing the energy consumption, reducing the total flight time and reducing the total delay is done. First, using the average path loss and UAV transmitted power functions, we present the optimal height of the UAV in the form of an optimization problem with a convex altitude range. Then, using the golden section search (GSS) algorithm and based on the condition of the function being unimodal, we calculate the optimization problem and obtain the optimal height value, which is the minimum of the average functions of the path loss and the transmitted power of the UAV. Also, using the convexity principle, we show that the average path loss function is convex in the mentioned interval. Next, using the relationship between the time to calculate the optimal height of the drone and the total flight time, we calculate the amount of energy consumed and the total delay. The simulation results using MATLAB show that the time to calculate the optimal height with the proposed algorithm is much faster than other methods. The time to calculate the optimal height in the proposed method is 0.03 s. The energy consumption using the proposed method is 53 kJ, and the flight time is 37 s, considering the stop on the way, which is the lowest value compared to other methods. Also, the total delay in the proposed method is less than in other methods. [ABSTRACT FROM AUTHOR]

In this paper, we study the Karush–Kuhn–Tucker optimality conditions in an optimization problem with interval-valued objective function on Hadamard manifolds. The gH-directional differentiability for interval-valued function is defined by using the generalized Hukuhara difference. The concepts of interval-valued convexity and pseudoconvexity are introduced on Hadamard manifolds, and several properties involving such functions are also given. Under these settings, we derive the KKT optimality conditions and give a numerical example to show that the results obtained in this paper are more general than the corresponding conclusions of Wu [The Karush–Kuhn–Tucker optimality conditions in an optimization problem with interval-valued objective function. Eur J Oper Res. 2007;176:46–59] in solving the optimization problem with interval-valued objective function. [ABSTRACT FROM AUTHOR]

In this paper, we propose new first-order methods for minimization of a convex function on a simple convex set. We assume that the objective function is a composite function given as a sum of a simple convex function and a convex function with inexact Hölder-continuous subgradient. We propose Universal Intermediate Gradient Method. Our method enjoys both the universality and intermediateness properties. Following the ideas of Nesterov (Math. Program. 152 (2015), pp. 381–404) on Universal Gradient Methods, our method does not require any information about the Hölder parameter and constant and adjusts itself automatically to the local level of smoothness. On the other hand, in the spirit of the Intermediate Gradient Method proposed by Devolder et al. (CORE Discussion Paper 2013/17, 2013), our method is intermediate in the sense that it interpolates between Universal Gradient Method and Universal Fast Gradient Method. This allows to balance the rate of convergence of the method and rate of the oracle error accumulation. Under the additional assumption of strong convexity of the objective, we show how the restart technique can be used to obtain an algorithm with faster rate of convergence. [ABSTRACT FROM AUTHOR]

The class of geometrically convex functions is a rich class that contains some important functions. In this paper, we further explore this class and present many interesting new properties, including fundamental inequalities, supermultiplicative type inequalities, Jensen-Mercer inequality, integral inequalities, and refined forms. The obtained results extend some celebrated results from the context of convexity to geometric convexity, with interesting applications to numerical inequalities for the hyperbolic and exponential functions. [ABSTRACT FROM AUTHOR]

Conjugate gradient methods are a class of very effective iterative methods for large-scale unconstrained optimization. In this paper, a new Dai-Liao conjugate gradient method for solving large-scale unconstrained optimization problem is proposed. Based on the approximately optimal stepsize for the gradient method, we derive three new choices for the important parameters tk in Dai-Liao conjugate gradient method. The search direction satisfies the sufficient descent condition, and the global convergences of the proposed method for uniformly convex and general functions are proved under some mild conditions. Numerical experiments on a set of test problems from the CUTEst library show that the proposed method is superior to some well-known conjugate gradient methods. [ABSTRACT FROM AUTHOR]

This paper deals with approximate solutions of a nonsmooth semi-infinite programming with multiple interval-valued objective functions. We first introduce four types of approximate quasi Pareto solutions of the considered problem by considering the lower-upper interval order relation and then apply some advanced tools of variational analysis and generalized differentiation to establish necessary optimality conditions for these approximate solutions. Sufficient conditions for approximate quasi Pareto solutions of such a problem are also provided by means of introducing the concepts of approximate (strictly) pseudo-quasi generalized convex functions defined in terms of the limiting subdifferential of locally Lipschitz functions. Finally, a Mond–Weir type dual model in approximate form is formulated, and weak, strong and converse-like duality relations are proposed. [ABSTRACT FROM AUTHOR]

In this paper, we introduce operator geodesically convex and operator convex-log functions and characterize some properties of them. Then we apply these classes of functions to present several operator Azcél and Minkowski-type inequalities extending some known results. The concavity counterparts are also considered. [ABSTRACT FROM AUTHOR]

This paper is concerned with the following partially dissipative viscoelastic Timoshenko system { ρ 1 ϕ t t − κ (ϕ x + ψ) x + κ ∫ 0 t g (t − s) (ϕ x + ψ) x d s = 0 in (0 , L) × R + , ρ 2 ψ t t − b ψ x x + κ (φ x + ψ) − κ ∫ 0 t g (t − s) (ϕ x + ψ) d s = 0 in (0 , L) × R + , with damping mechanism acting only on the shear force, and with Dirichlet boundary conditions. We consider a very general relaxation function g ′ (t) ≤ − ξ (t) H (g (t)) , ∀ t ≥ 0. Under appropriate conditions on ξ and H, we establish a general stability result. The result is obtained under the assumption of equal speed of wave propagation. This work extends and generalizes many results in literature such as Alves et al. [On modeling and uniform stability of a partially dissipative viscoelastic Timoshenko system. SIAM J Math Anal. 2019;51(6):4520–4543]. [ABSTRACT FROM AUTHOR]

This paper deals with a nonlinear viscoelastic equation. The aim is to expand the class of the function of relaxation h (t) that ensuring a general decay. We adopt the following commonly condition on relaxation function h (t) : h ′ (t) ≤ − ξ (t) χ (h (t)) , where ξ is a nonincreasing function and χ is an increasing and convex function on the whole [ 0 , ∞) instead of is convex only near the origin in the literature. [ABSTRACT FROM AUTHOR]

The widely known hermite-hadamard-Fejer type inequalities are so important in the field of mathematical analysis. Many researchers have studied on these inequalities. In this paper, we have obtained several inequalities related to the Hermite-Hadamard inequality for a special class of the functions called uniformly convex functions. We have also presented applications of these obtained inequalities in some error estimates for higher moments of random variables. [ABSTRACT FROM AUTHOR]

This paper deals with quasidifferentiable vector optimization problems involving invex functions wrt convex compact sets. We present vector variational-like inequalities of Minty type and of Stampacchia type in terms of quasidifferentials denoted by (QMVVLI) and (QSVVLI), respectively. By utilizing these variational inequalities, we infer vital and adequate optimality conditions for an efficient solution of the quasidifferentiable vector optimization problem involving invex functions wrt convex compact sets. We also establish various results for the solutions of the corresponding weak versions of the vector variational-like inequalities in terms of quasidifferentials. [ABSTRACT FROM AUTHOR]

This paper is concerned with second-order optimality conditions for Tikhonov regularized optimal control problems governed by the obstacle problem. Using a simple observation that allows to characterize the structure of optimal controls on the active set, we derive various conditions that guarantee the local/global optimality of first-order stationary points and/or the local/global quadratic growth of the reduced objective function. Our analysis extends and refines existing results from the literature and also covers those situations where the problem at hand involves additional box-constraints on the control. As a byproduct, our approach shows in particular that Tikhonov regularized optimal control problems for the obstacle problem can be reformulated as state-constrained optimal control problems for the Poisson equation and that problems involving a subharmonic obstacle and a convex objective function are uniquely solvable. The paper concludes with three counterexamples which illustrate that rather peculiar effects can occur in the analysis of second-order optimality conditions for optimal control problems governed by the obstacle problem and that necessary second-order conditions for such problems may be hard to derive. [ABSTRACT FROM AUTHOR]

This paper addresses the bicriteria scheduling problems with simultaneous consideration of job rejection, controllable processing times and rate-modifying activity on a single machine. A job is either rejected, in which case a rejection penalty will be incurred, or accepted and processed on the machine. The rate-modifying activity is an activity on the machine that changes the processing times of the jobs scheduled after the activity. The processing time of a job scheduled after the rate-modifying activity decreases with a job-dependent factor. The processing time of each job can also be controlled by allocating extra resource which is either a linear or a convex function of the amount of a common continuously divisible resource allocated to the job. The objective is to determine the rejected job set, the accepted job sequence, the time (location) of the rate-modifying activity and the resource allocation that jointly find the trade-off between two criteria, where the first criterion is measured as the sum of total completion time and resource consumption cost while the second criterion is the total rejection cost. We consider four different models for treating the two criteria. The computational complexity status and solution procedures are provided for the problems under consideration. [ABSTRACT FROM AUTHOR]

In this paper, we first consider the split common null point problem in two Banach spaces. Then, using the Bregman generalized resolvents of maximal monotone operators, we prove strong convergence theorems of Halpern type iteration for finding a solution of the split common null point problem in two Banach spaces. As an application of our result, we study the split equilibrium problem in general Banach spaces and approximate a solution of the problem for the first time. Our new technique is based on basic properties of a Bregman distance induced by a Bregman function without using Bregman projection or the requirement of Mosco convergence of the sequences produced by the method. It is well known that the Bregman distance does not satisfy either the symmetry property or the triangle inequality which are required for standard distances. So, the results of the paper improve and extend many recent results in the literature. [ABSTRACT FROM AUTHOR]

The alternating direction method of multipliers (ADMM) is an efficient method for solving separable problems. However, ADMM may not converge when there is a nonconvex function in the objective. The main contributions of this paper are proposing and analysing an inertial proximal ADMM for a class of nonconvex optimization problems. The proposed algorithm combines the basic ideas of the proximal ADMM and the inertial proximal point method. The global and strong convergence of the proposed algorithm is analysed under mild conditions. Finally, we give some preliminary numerical results to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes an online constrained extremum-seeking approach for an unknown convex function with unknown constraints within a class of uncertain dynamical systems with an available output disturbed by a stochastic noise. It is assumed that the objective function along with the constraints are available for measurement. The main contribution of the paper is the formulation of the problem using the penalty method and the development of an extremum seeking algorithm based on a modified synchronous detection method for computing a stochastic gradient descent procedure. In order to reject the undesirable uncertainties and perturbations of the dynamic plant from the beginning of the process, we employ the standard deterministic Integral Sliding Mode Control transforming the initial dynamic plant to static one. Then, we apply the gradient decedent technique. We consider time varying parameters of the suggested procedure for compensating the unknown dynamics. To validate the exposition, we perform a numerical example simulation. [ABSTRACT FROM AUTHOR]

For the first time in this paper, the dual problem is constructed for the problem with generalized first order partial differential inclusions, the duality theorem is proved. For discrete problems, necessary and sufficient optimality conditions are derived in the form of the Euler–Lagrange type inclusion. Thus, it is possible to construct dual problems for problems with partial differential inclusions on the basis of dual operations of addition and infimal convolution of convex functions. To pass from the dual problem to the discrete-approximation problem, important equivalence theorems are proved, without which it is unlikely that certain success can be achieved along this path. Hence, we believe that this method of constructing dual problems can serve as the only possible method for studying duality for a wide class of problems with partial/ordinary differential inclusions. The results obtained are demonstrated on some linear problem and on a problem with first-order polyhedral partial differential inclusions. [ABSTRACT FROM AUTHOR]

In this paper, we firstly establish new numerical radius inequalities which refine a result of Kittaneh in [Studia Math. 168, 73–80 (2005)], then present some numerical radius inequalities involving non-negative increasing convex functions for n × n operator matrices, which generalize the related results of Shebrawi in [Linear Algebra Appl. 523(15), 1–12 (2017)]. [ABSTRACT FROM AUTHOR]

In this paper, we study a non-convex min–max fractional problem of quadratic functions subject to convex and non-convex quadratic constraints. First, by using the Dinkelbach-type method, we transform the fractional problem into a univariate nonlinear equation. To evaluate this equation, we need to solve a non-convex quadratically constrained quadratic programming (QCQP) problem. To solve this problem, we propose a new method. In the proposed method, first, by using relaxation and convexification of non-convex constraints of non-convex QCQP problem, an upper bound and a lower bound of the optimal value is obtained. By using these bounds, we construct a parametric QCQP problem with two constraints. Then, by solution of the new problem, the parameters of this problem are updated for the next iteration. We show that the sequence of solutions of new problems is convergent to a global optimal solution of the non-convex QCQP problem. Numerical results are given to show the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose new numerical algorithms in the setting of unconstrained optimization problems and we prove the discrete rate of convergence of order O 1 / n 2 in the iterates of the convex objective function. Our optimization algorithms are obtained via discretizations from dynamical systems with Hessian-driven damping. Finally, some numerical experiments are presented in order to validate the theoretical results. [ABSTRACT FROM AUTHOR]

For , let denote the class of sense preserving harmonic mappings in the unit disk satisfying . The main aim of this paper is to study some basic properties such as coefficient bounds, growth estimates and convolution for functions in the class . We end the paper with an application, and construct harmonic univalent polynomials belonging to . [ABSTRACT FROM AUTHOR]

Based on a coupling conjugation scheme and the perturbational approach, we build Fenchel–Lagrange dual problem of a composed optimization model with infinite constraints in separated locally convex spaces. This paper has mainly two targets. One is to establish strong duality under a new regularity condition (RCA) and an extension closed-type condition (ECRCA). The e-convex counterpart of Fenchel–Moreau theorem plays a key role in analysing the relation between them. The other aim is to achieve the sufficient and necessary characterizations for total duality in terms of c-subdifferentials. For this purpose, a formula for ε-c-subdifferentials of a proper function composed with a linear continuous operator is proved and applied. [ABSTRACT FROM AUTHOR]

In this paper, we study a nonsmooth/nonconvex multiobjective optimization problem with uncertain constraints in arbitrary Asplund spaces. We first provide necessary optimality condition in a fuzzy form for approximate weakly robust efficient solutions and then establish necessary optimality theorem for approximate weakly robust quasi-efficient solutions of the problem in the sense of the limiting subdifferential by exploiting a fuzzy optimality condition in terms of the Fréchet subdifferential. Sufficient conditions for approximate (weakly) robust quasi-efficient solutions to such a problem are also driven under the new concept of generalized pseudo convex functions. Finally, we address an approximate MondWeir-type dual robust problem to the reference problem and explore weak, strong, and converse duality properties under assumptions of pseudo convexity. [ABSTRACT FROM AUTHOR]

In this paper, by using the inexact scalarization proximal methods, we solve quasiconvex multiobjective optimization problems on Hadamard manifolds. Under some assumptions on the multifunction of problem and vector fields, our methods are proved to be convergent to a Pareto critical point of the problem. For the convex case, the sequences generated by the methods converge to a weak Pareto solution. [ABSTRACT FROM AUTHOR]

In this paper, we consider a location-scale family arising out of Absolutely Continuous Trivariate Exponential (ACTVE) distribution with equal marginal due to Weier and Basu. The distribution of the complete sufficient statistic is obtained by first proving a result on spacings similar to the results of Sukhatme for univariate exponential distribution. The UMRUE with respect to any loss function convex in the second argument of the location – scale parameter is obtained. Following the simultaneous equivariant estimation approach of Edwin Prabhakaran and Chandrasekar, we derive the minimum risk equivariant estimator of the location -scale parameter. Further the equivariant estimation of percentiles of the population is also discussed. UMP tests for ACTVE location-scale family are also derived. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the Douglas–Rachford method (DR) for two closed (possibly nonconvex) sets in Euclidean spaces. We show that under certain regularity conditions, the DR converges locally with-linear rate. In convex settings, we prove that the linear convergence is global. Our study recovers recent results on the same topic. [ABSTRACT FROM AUTHOR]

The class of nonsmooth codifferentiable functions was introduced by professor V.F. Demyanov in the late 1980s. He also proposed a method for minimizing these functions called the method of codifferential descent (MCD). However, until now almost no theoretical results on the performance of this method on particular classes of nonsmooth optimization problems were known. In the first part of the paper, we study the performance of the method of codifferential descent on a class of nonsmooth convex functions satisfying some regularity assumptions, which in the smooth case are reduced to the Lipschitz continuity of the gradient. We prove that in this case the MCD has the iteration complexity bound O (1 / ε). In the second part of the paper we obtain new global optimality conditions for piecewise affine functions in terms of codifferentials. With the use of these conditions we propose a modification of the MCD for minimizing piecewise affine functions (called the method of global codifferential descent) that does not use line search, and discards those 'pieces' of the objective functions that are no longer useful for the optimization process. Then we prove that the MCD as well as its modification proposed in the article find a point of global minimum of a nonconvex piecewise affine function in a finite number of steps. [ABSTRACT FROM AUTHOR]

In this paper, we study distributed multiagent optimisation over undirected graphs. The optimisation problem is to minimise a global objective function, which is composed of the sum of a set of local objective functions. Recent researches on this problem have made significant progress by using primal-dual methods. However, the inner link among different algorithms is unclear. This paper shows that some state-of-the-art algorithms differ in that they incorporate the slightly different last dual gradient terms based on the augmented Lagrangian analysis. Then, we propose a distributed Nesterov accelerated optimisation algorithm, where a doubly stochastic matrix is allowed to use, and nonidentical local step-sizes are employed. We analyse the convergence of the proposed algorithm by using the generalised small gain theorem under the assumption that each local objective function is strongly convex and has Lipschitz continuous gradient. We prove that the sequence generated by the proposed algorithm linearly converge to an optimal solution if the largest step-size is positive and less than an explicitly estimated upper bound, and the largest momentum parameter is nonnegative and less than an upper bound determined by the largest step-size. Simulation results further illustrate the efficacy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we present a quasi-convex optimisation method to minimise an upper bound of the dwell time for stability of switched delay systems. Piecewise Lyapunov–Krasovskii functionals are introduced and the upper bound for the derivative of Lyapunov functionals is estimated by free-weighting matrices method to investigate non-switching stability of each candidate subsystems. Then, a sufficient condition for the dwell time is derived to guarantee the asymptotic stability of the switched delay system. Once these conditions are represented by a set of linear matrix inequalities , dwell time optimisation problem can be formulated as a standard quasi-convex optimisation problem. Numerical examples are given to illustrate the improvements over previously obtained dwell time bounds. Using the results obtained in the stability case, we present a nonlinear minimisation algorithm to synthesise the dwell time minimiser controllers. The algorithm solves the problem with successive linearisation of nonlinear conditions. [ABSTRACT FROM AUTHOR]

The paper provides objective motivation for the class of non–local moment prior, introduced for the purposes of Bayesian hypothesis testing using Bayes factors. The motivation is that it minimizes Fisher information among a large class of density. Being a minimizer of some measure of information is an important feature so that the test, even within a Bayesian framework, has an objective criterion. [ABSTRACT FROM AUTHOR]

In this paper, we consider gradient methods for minimizing smooth convex functions, which employ the information obtained at the previous iterations in order to accelerate the convergence towards the optimal solution. This information is used in the form of a piece-wise linear model of the objective function, which provides us with much better prediction abilities as compared with the standard linear model. To the best of our knowledge, this approach was never really applied in Convex Minimization to differentiable functions in view of the high complexity of the corresponding auxiliary problems. However, we show that all necessary computations can be done very efficiently. Consequently, we get new optimization methods, which are better than the usual Gradient Methods both in the number of oracle calls and in the computational time. Our theoretical conclusions are confirmed by preliminary computational experiments. [ABSTRACT FROM AUTHOR]

This paper fully studies distributed optimal consensus problem in undirected dynamical networks. We consider a group of networked agents that are supposed to rendezvous at the optimal point of a collective convex objective function. Each agent has no knowledge about the global objective function and only has access to its own local objective function, which is a portion of the global one, and states information of agents within its neighbourhood set. In this setup, all agents coordinate with their neighbours to seek the consensus point that minimises the network's global objective function. In the current paper, we consider agents with single-integrator and double-integrator dynamics. Further, it is supposed that agents' movements are limited by some convex inequality constraints. In order to find the optimal consensus point under the described scenario, we combine the interior-point optimisation algorithm with a consensus protocol and propose a distributed control law. The associated convergence analysis based on Lyapunov stability analysis is provided. [ABSTRACT FROM AUTHOR]