Optimization Journal Impact Factor & Information

Publisher: Taylor & Francis

Journal description

Optimization publishes refereed, theoretical and applied papers on the latest developments in fields such as linear, nonlinear, stochastic, parametric, discrete and dynamic programming, control theory and game theory. A special section is devoted to review papers on theory and methods in interesting areas of mathematical programming and optimization techniques. The journal also publishes conference proceedings, book reviews and announcements.

Current impact factor: 0.77

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 Impact Factor 0.771
2012 Impact Factor 0.707
2011 Impact Factor 0.5
2010 Impact Factor 0.509
2009 Impact Factor 0.616
2008 Impact Factor 0.845
2007 Impact Factor 0.408
2006 Impact Factor 0.5
2005 Impact Factor 0.325
2004 Impact Factor 0.33
2003 Impact Factor 0.206

Impact factor over time

Impact factor

Additional details

5-year impact 0.70
Cited half-life 9.50
Immediacy index 0.11
Eigenfactor 0.00
Article influence 0.47
Website Optimization website
Other titles Optimization (Online)
ISSN 0233-1934
OCLC 50446924
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Taylor & Francis

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Some individual journals may have policies prohibiting pre-print archiving
    • On author's personal website or departmental website immediately
    • On institutional repository or subject-based repository after either 12 months embargo
    • Publisher's version/PDF cannot be used
    • On a non-profit server
    • Published source must be acknowledged
    • Must link to publisher version
    • Set statements to accompany deposits (see policy)
    • The publisher will deposit in on behalf of authors to a designated institutional repository including PubMed Central, where a deposit agreement exists with the repository
    • STM: Science, Technology and Medicine
    • Publisher last contacted on 25/03/2014
    • This policy is an exception to the default policies of 'Taylor & Francis'
  • Classification
    ​ green

Publications in this journal

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We survey incremental methods for minimizing a sum m i=1 f i (x) consisting of a large number of convex component functions f i . Our methods consist of iterations applied to single components, and have proved very effective in practice. We introduce a unified algorithmic framework for a variety of such methods, some involving gradient and subgradient iterations, which are known, and some involving combinations of subgradient and proximal methods, which are new and offer greater flexibility in exploiting the special structure of f i . We provide an analysis of the convergence and rate of convergence properties of these methods, including the advantages offered by randomization in the selection of components. We also survey applications in inference/machine learning, signal processing, and large-scale and distributed optimization.
    Optimization 07/2015;
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    ABSTRACT: The split equality problem has extraordinary utility and broad applicability in many areas of applied mathematics. Recently, Moudafi proposed an alternating CQ algorithm and its relaxed variant to solve it. However, to employ Moudafi’s algorithms, one needs to know a priori norm (or at least an estimate of the norm) of the bounded linear operators (matrices in the finite-dimensional framework). To estimate the norm of an operator is very difficult, but not an impossible task. It is the purpose of this paper to introduce a projection algorithm with a way of selecting the stepsizes such that the implementation of the algorithm does not need any priori information about the operator norms. We also practise this way of selecting stepsizes for variants of the projection algorithm, including a relaxed projection algorithm where the two closed convex sets are both level sets of convex functions, and a viscosity algorithm. Both weak and strong convergence are investigated.
    Optimization 06/2015; 64(9). DOI:10.1080/02331934.2014.895897
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    ABSTRACT: The minimal rank solutions of continuous-time and discrete-time symmetric Lyapunov equations, which have important applications in dynamical systems, are generally difficult to achieve due to the involved rank minimization. By employing the decomposition techniques of Euclidean Jordan algebra and the symmetric Lyapunov operators, we show that in the setting of Euclidean Jordan algebra, these minimal rank solutions of both symmetric continuous-time and discrete-time Lyapunov equations are unique and can be exactly solved by the corresponding Schatten -norm () relaxation problems under some easy-to-check conditions. Moreover, both the upper and lower bounds for the minimal ranks are proposed.
    Optimization 06/2015; DOI:10.1080/02331934.2015.1053882
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    ABSTRACT: In this paper, the classical KKT, complementarity and Lagrangian saddle-point conditions are generalized to obtain equivalent conditions characterizing the optimality of a feasible solution to a general linear semi-infinite programming problem without constraint qualifications. The method of this paper differs from the usual convex analysis methods and its main idea is rooted in some fundamental properties of linear programming.
    Optimization 06/2015; DOI:10.1080/02331934.2015.1051533
  • Optimization 06/2015; 64(6). DOI:10.1080/02331934.2015.1027530
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    ABSTRACT: We deliver formulae for the biconjugate functions of some infimal functions that hold provided the fulfilment of weak regularity conditions of both closedness and interiority types. As special cases, we obtain biconjugates of infimal convolutions of finitely many functions, of optimal value functions of both constrained and unconstrained optimization problems as well as of marginal functions associated with multifunctions (that can be, for instance, convex processes) and some given functions. Moreover, we rediscover or extend different results on biconjugate functions from the literature.
    Optimization 05/2015; 64(8):1-17. DOI:10.1080/02331934.2015.1046873
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    ABSTRACT: It is known that, in finite dimensions, the support function of a compact convex set with nonempty interior is differentiable excepting the origin if and only if the set is strictly convex. In this paper, we realize a thorough study of the relations between the differentiability of the support function on the interior of its domain and the convexity of the set, mainly for unbounded sets. Then, we revisit some results related to the differentiability of the cost function associated to a production function.
    Optimization 05/2015; DOI:10.1080/02331934.2015.1044528
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    ABSTRACT: In this paper, we establish the connectedness of the sets of Henig efficient solutions, globally efficient solutions, weak efficient solutions, superefficient solutions and efficient solutions for a class of generalized vector equilibrium problems without the assumptions of monotonicity and compactness.
    Optimization 05/2015; DOI:10.1080/02331934.2015.1044899
  • Optimization 05/2015; DOI:10.1080/02331934.2015.1032286
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    ABSTRACT: In decision-making problems where uncertainty plays a key role and decisions have to be taken prior to observing uncertainty, chance constraints are a strong modelling tool for defining safety of decisions. These constraints request that a random inequality system depending on a decision vector has to be satisfied with a high probability. The characteristics of the feasible set of such chance constraints depend on the constraint mapping of the random inequality system, the underlying law of uncertainty and the probability level. One characteristic of particular interest is convexity. Convexity can be shown under fairly general conditions on the underlying law of uncertainty and on the constraint mapping, regardless of the probability-level. In some situations, convexity can only be shown when the probability-level is high enough. This is defined as eventual convexity. In this paper, we will investigate further how eventual convexity can be assured for specially structured chance constraints involving Copulae. The Copulae have to exhibit generalized concavity properties. In particular, we will extend recent results and exhibit a clear link between the generalized concavity properties of the various mappings involved in the chance constraint for the result to hold. Various examples show the strength of the provided generalization.
    Optimization 05/2015; 64(5):1263-1284. DOI:10.1080/02331934.2013.855211
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    ABSTRACT: The notion of quasi-relative interior was introduced by Borwein and Lewis in 1992 and applied for duality results in partially finite convex optimization problems. In the last 10 years, several articles were dedicated to duality results in infinite-dimensional scalar, vector and set-valued optimization problems using this notion. The aim of this paper is to refine and discuss such results. We do this observing that the notion of quasi-relative interior is related to (non-proper) separation of a convex set and some of its elements, then pointing out the relation between the subdifferentiability of a function associated to a set of epigraph type at a certain point and the fact that a corresponding point is not in the quasi-relative interior of the closed convex hull of the set.
    Optimization 04/2015; 64(8):1-29. DOI:10.1080/02331934.2015.1032284
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    ABSTRACT: In the paper we develop a method for converting a (not necessarily uniformly bounded) upper (lower) primal exhauster of a continuous positively homogeneous function to a lower (upper) primal exhauster of the same function. The method is based on representation of a continuous positively homogeneous function as a pointwise supremum (infimum) of an one-parameter monotone family of Lipschitz continuous positively homogeneous functions that is of independent interest.
    Optimization 04/2015; DOI:10.1080/02331934.2015.1032285
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    ABSTRACT: In this paper, we prove a strong convergence theorem for finding a common element of the set of solutions of an equilibrium problem and the set of fixed points of a symmetric generalized hybrid mapping by using modified hybrid method. Our results extend and improve some existing results in the literature.
    Optimization 04/2015; DOI:10.1080/02331934.2015.1032283
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    ABSTRACT: In this paper, we consider the split common null point problem with resolvents of maximal monotone operators in Banach spaces. Then, using the shrinking projection method, we prove a strong convergence theorem for finding a solution of the split common null point problem in Banach spaces.
    Optimization 03/2015; DOI:10.1080/02331934.2015.1020943