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  • 1
    ISSN: 1436-4646
    Keywords: Key words: basis recovery – partition – principal pivot transforms – Balinski-Tucker tableaus – quadratic programming – linear complementarity problems – interior point methods – sufficient matrices – crossover – Criss-Cross method
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract. Optimal solutions of interior point algorithms for linear and quadratic programming and linear complementarity problems provide maximally complementary solutions. Maximally complementary solutions can be characterized by optimal partitions. On the other hand, the solutions provided by simplex–based pivot algorithms are given in terms of complementary bases. A basis identification algorithm is an algorithm which generates a complementary basis, starting from any complementary solution. A partition identification algorithm is an algorithm which generates a maximally complementary solution (and its corresponding partition), starting from any complementary solution. In linear programming such algorithms were respectively proposed by Megiddo in 1991 and Balinski and Tucker in 1969. In this paper we will present identification algorithms for quadratic programming and linear complementarity problems with sufficient matrices. The presented algorithms are based on the principal pivot transform and the orthogonality property of basis tableaus.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Annals of operations research 46-47 (1993), S. 203-233 
    ISSN: 1572-9338
    Keywords: Linear programming ; simplex method ; pivot rules ; cycling ; recursion ; minimal index rule ; parametric programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract The purpose of this paper is to discuss the various pivot rules of the simplex method and its variants that have been developed in the last two decades, starting from the appearance of the minimal index rule of Bland. We are mainly concerned with finiteness properties of simplex type pivot rules. Well known classical results concerning the simplex method are not considered in this survey, but the connection between the new pivot methods and the classical ones, if there is any, is discussed. In this paper we discuss three classes of recently developed pivot rules for linear programming. The first and largest class is the class of essentially combinatorial pivot rules including minimal index type rules and recursive rules. These rules only use labeling and signs of the variables. The second class contains those pivot rules which can actually be considered as variants or generalizations or specializations of Lemke's method, and so they are closely related to parametric programming. The last class has the common feature that the rules all have close connections to certain interior point methods. Finally, we mention some open problems for future research.
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical methods of operations research 44 (1996), S. 11-30 
    ISSN: 1432-5217
    Keywords: Interior-point method ; affine scaling method ; primal-dual method ; long-step method
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract In this paper we propose a long-step target-following methodology for linear programming. This is a general framework, that enables us to analyze various long-step primal-dual algorithms in the literature in a short and uniform way. Among these are long-step central and weighted path-following methods and algorithms to compute a central point or a weighted center. Moreover, we use it to analyze a method with the property that starting from an initial noncentral point, generates iterates that simultaneously get closer to optimality and closer to centrality.
    Type of Medium: Electronic Resource
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  • 4
    ISSN: 1573-2916
    Keywords: Copositive programming ; Global maximization ; Positive semidefinite matrices ; Standard quadratic optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract A standard quadratic problem consists of finding global maximizers of a quadratic form over the standard simplex. In this paper, the usual semidefinite programming relaxation is strengthened by replacing the cone of positive semidefinite matrices by the cone of completely positive matrices (the positive semidefinite matrices which allow a factorization FF T where F is some non-negative matrix). The dual of this cone is the cone of copositive matrices (i.e., those matrices which yield a non-negative quadratic form on the positive orthant). This conic formulation allows us to employ primal-dual affine-scaling directions. Furthermore, these approaches are combined with an evolutionary dynamics algorithm which generates primal-feasible paths along which the objective is monotonically improved until a local solution is reached. In particular, the primal-dual affine scaling directions are used to escape from local maxima encountered during the evolutionary dynamics phase.
    Type of Medium: Electronic Resource
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  • 5
    Book
    Book
    Dordrecht :Kluwer Academic Publishers,
    Title: Interior point methods of mathematical programming; 5
    Contributer: Terlaky, Tamas
    Publisher: Dordrecht :Kluwer Academic Publishers,
    Year of publication: 1996
    Pages: 528 S.
    Series Statement: Applied optimization 5
    Type of Medium: Book
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  • 6
    Title: Theory and algorithms for linear optimization : an interior point approach
    Author: Roos, Kees
    Contributer: Terlaky, Tamas , Vial, Jean-Philippe
    Publisher: Chichester u.a. :Wiley,
    Year of publication: 1997
    Pages: 482 S.
    Series Statement: Wiley interscience series in discrete mathematics and optimization
    Type of Medium: Book
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