Some Notes on a Minimization Problem

Let X[a,b] be a compact set containing at least n+1 points and Kan n-dimensional Haar subspace in c[a,b]. Let F(x,y) be a nonnegativefunction, defined on X×(-∞,∞), satisfying ‖F(·,p)‖<∞ with the L_∞norm forsome∈K, where F(x,p)≡F(x,p(x)). The minimization problem discussed in this paper is to find an elementp∈K such that ‖F(·,p)‖=inf ‖F(·,q)‖, such an element p(if any) is saidto be a minimum to F in K~(q∈K). The author in [1,2] studied this problem and has given the main theoremsin the Cbebyshev theory under the following assumptions: (A) lim F(x,y)=∞, x∈X; (B) lim F(x,u)=F(x,y), x∈X,y; (C)lim F(u,υ)=F(x,y),x∈X,y; (D) For each x∈X there existtwo real numbers f~-(x) and f~+(x),f~-(x)f~+(x). such that F(x,y) is strictlydecreasing with respect to y on (-∞,f~-(x)] and strictly increasing on [f~+(x),∞), and F(x,y)=F(x):=inf F(x,υ) on [f~-(x),f~+(x)]. Denote f_1(x)=inf{y:F(x,y)‖F~*‖},f_2(x)=sup{y:F(x,) ‖F‖},f_1(x)=lim f_1(u),f_2(x)=lim f_2(u), G=(q∈K: f_1qf_2}.For pεK set X_p={     

Vector Minimization of Upper Robust Mappings

1.IntroductionLetXbeatopologicalspaceandf=(f',f',...):X-Reamapping.Inthispaper,weconsiderthevectoroptimizationproblemswithrespecttothepartialorderinginducedb}'thenonnegativeorthantoftheproductspaceRe.Apointx6Xissaidtobeaegcientsolutionornondominatedsoluti…     

Generalized γ-Valid Cut Procedure for Concave Minimization

The concept of a -valid cutting plane has been used in many types of algorithms for solving concave minimization problems. Unfortunately, the procedures proposed to date for constructing these cuts are valid only under certain assumptions that often may not hold in practice. Chief among these is the requirement that the feasible region of the concave minimization problem in question have full dimension, and that the objective function of this problem be concave rather than quasiconcave. In this article, we propose, validate, and show how to implement a more general -valid cutting plane procedure which eliminates these restrictions.     

A Projection Proximal-Point Algorithm for ℓ1 Minimization

The problem of the minimization of least squares functionals with ?¹ penalties is considered in an infinite dimensional Hilbert space setting. Though there are several algorithms available in the finite dimensional setting there are only a few of them that come with a proper convergence analysis in the infinite dimensional setting.

In this work we provide an algorithm from a class that has not been considered for ?¹ minimization before, namely, a proximal-point method in combination with a projection step. We show that this idea gives a simple and easy-to-implement algorithm. We present experiments that indicate that the algorithm may perform better than other algorithms if we employ them without any special tricks. Hence, we may conclude that the projection proximal-point idea is a promising idea in the context of ?¹ minimization.     

Globally Convergent Cutting Plane Method for Nonconvex Nonsmooth Minimization

Nowadays, solving nonsmooth (not necessarily differentiable) optimization problems plays a very important role in many areas of industrial applications. Most of the algorithms developed so far deal only with nonsmooth convex functions. In this paper, we propose a new algorithm for solving nonsmooth optimization problems that are not assumed to be convex. The algorithm combines the traditional cutting plane method with some features of bundle methods, and the search direction calculation of feasible direction interior point algorithm (Herskovits, J. Optim. Theory Appl. 99(1):121–146, 1998). The algorithm to be presented generates a sequence of interior points to the epigraph of the objective function. The accumulation points of this sequence are solutions to the original problem. We prove the global convergence of the method for locally Lipschitz continuous functions and give some preliminary results from numerical experiments.     

Forward–Backward Penalty Scheme for Constrained Convex Minimization Without Inf-Compactness

In order to solve constrained minimization problems, Attouch et al. propose a forward–backward algorithm that involves an exterior penalization scheme in the forward step. They prove that every sequence generated by the algorithm converges weakly to a solution of the minimization problem if either the objective function or the penalization function corresponding to the feasible set is inf-compact. Unfortunately, this assumption leaves out problems that are not coercive, as well as several interesting applications in infinite-dimensional spaces. The purpose of this short article is to show this convergence result without the inf-compactness assumption.     

The Overlay of Minimization Diagrams in a Randomized Incremental Construction

In a randomized incremental construction of the minimization diagram of a collection of n hyperplanes in ℝ ^d , for d≥2, the hyperplanes are inserted one by one, in a random order, and the minimization diagram is updated after each insertion. We show that if we retain all the versions of the diagram, without removing any old feature that is now replaced by new features, the expected combinatorial complexity of the resulting overlay does not grow significantly. Specifically, this complexity is O(n ^⌊d/2⌋log n), for d odd, and O(n ^⌊d/2⌋), for d even. The bound is asymptotically tight in the worst case for d even, and we show that this is also the case for d=3. Several implications of this bound, mainly its relation to approximate halfspace range counting, are also discussed.     

A Note on Single Machine Scheduling with Coefficient of Completion Time Variation Minimization

1.IntroductionGivenasetN~{1,2,...,n},n>1,ofindependentandsimultaneouslyavailablejobswhicharetobeprocessednonpreemptivelyonasinglemachine,jobirequiresapositiveprocessingtimepiandisassignedapositiveweightfi,iEN.Let11bethesetofn!possiblepermutationsofintegers1,2,...,n,and7~{[l],[2]!...I[n]}E11beajobsequencewhichdenotesthatjob[k]isthekthjobtobeprocessedonthemachine.Assumethatthemachinestratstoprocessthefirstjobattimezero.TheproblemistofindajobsequenceT*E11tominimizewhereC[i]isthecompletiontime…     

Convergence Properties of Dikin’s Affine Scaling Algorithm for Nonconvex Quadratic Minimization

We study convergence properties of Dikins affine scaling algorithm applied to nonconvex quadratic minimization. First, we show that the objective function value either diverges or converges Q-linearly to a limit. Using this result, we show that, in the case of box constraints, the iterates converge to a unique point satisfying first-order and weak second-order optimality conditions, assuming the objective function Hessian Q is rank dominant with respect to the principal submatrices that are maximally positive semidefinite. Such Q include matrices that are positive semidefinite or negative semidefinite or nondegenerate or have negative diagonals. Preliminary numerical experience is reported.     

By How Much Can Residual Minimization Accelerate the Convergence of Orthogonal Residual Methods?

We capitalize upon the known relationship between pairs of orthogonal and minimal residual methods (or, biorthogonal and quasi-minimal residual methods) in order to estimate how much smaller the residuals or quasi-residuals of the minimizing methods can be compared to those of the corresponding Galerkin or Petrov–Galerkin method. Examples of such pairs are the conjugate gradient (CG) and the conjugate residual (CR) methods, the full orthogonalization method (FOM) and the generalized minimal residual (GMRES) method, the CGNE and BiCG versions of applying CG to the normal equations, as well as the biconjugate gradient (BiCG) and the quasi-minimal residual (QMR) methods. Also the pairs consisting of the (bi)conjugate gradient squared (CGS) and the transpose-free QMR (TFQMR) methods can be added to this list if the residuals at half-steps are included, and further examples can be created easily.The analysis is more generally applicable to the minimal residual (MR) and quasi-minimal residual (QMR) smoothing processes, which are known to provide the transition from the results of the first method of such a pair to those of the second one. By an interpretation of these smoothing processes in coordinate space we deepen the understanding of some of the underlying relationships and introduce a unifying framework for minimal residual and quasi-minimal residual smoothing. This framework includes the general notion of QMR-type methods.     

A Dual Approach for Solving Nonlinear Infinity-Norm Minimization Problems with Applications in Separable Cases

In this paper,we consider nonlinear infinity-norm minimization problems.We device a reliable Lagrangian dual approach for solving this kind of problems and based on this method we propose an algorithm for the mixed linear and nonlinear infinity- norm minimization problems.Numerical results are presented.     

Minimization and error estimates for a class of the nonlinear Schrödinger eigenvalue problems

It is shown that the nonlinear eigenvalue problem can be transformed into a constrained functional problem. The corresponding minimal function is a weak solution of this nonlinear problem. In this paper, one type of the energy functional for a class of the nonlinear Schrödinger eigenvalue problems is proposed, the existence of the minimizing solution is proved and the error estimate is given out.     

Minimization of asymptotic energy of Müller's functional endowed with local nonlinear lower-order term

We solve a minimization problem associated to a generalization of the Müller functional studied in the paper G. Alberti, S. Müller: A new approach to variational problems with multiple scales, Comm. Pure Appl. Math. 54 , 761–825 (2001), whereby the lower order term ∫¹₀a(s)v²(s)ds (involving a primitive of the mass density function, v = v(s) , and the weight function a = a(s) ) is replaced by ∫¹₀a(s, v(s), v′(s))v²(s)ds (where a belongs to a suitable Carathéodory class). We calculate the rescaled asymptotic energy of the functional as small parameter epsilon tends to zero. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Hybrid Moreau’s Proximal Algorithms and Convergence Theorems for Minimization Problems in Hilbert Spaces with Applications

Abstract

In this paper, motivated by Moreau’s proximal algorithm, we give several algorithms and related weak and strong convergence theorems for minimization problems under suitable conditions. These algorithms and convergence theorems are different from the results in the literatures. Besides, we also study algorithms and convergence theorems for the split feasibility problem in real Hilbert spaces. Finally, we give numerical results for our main results.     

Initiation of Cracks in Griffith’s Theory: An Argument of Continuity in Favor of Global Minimization

The initiation of a crack in a sound body is a real issue in the setting of Griffith’s theory of brittle fracture. If one uses the concept of critical energy release rate (Griffith’s criterion), it is in general impossible to initiate a crack. On the other hand, if we replace it by a least energy principle (Francfort–Marigo’s criterion), it becomes possible to predict the onset of cracking in any circumstance. However this latter criterion can appear too strong. We propose here to reinforce its interest by an argument of continuity. Specifically, we consider the issue of the initiation of a crack at a notch whose angle ω is considered as a parameter. The result predicted by the Griffith criterion is not continuous with respect to ω, since no initiation occurs when ω>0 while a crack initiates when ω=0. In contrast, the Francfort–Marigo’s criterion delivers a response which is continuous with respect to ω, even though the onset of cracking is necessarily brutal when ω>0. The theoretical analysis is illustrated by numerical computations.