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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Algorithmica 12 (1994), S. 436-457 
    ISSN: 1432-0541
    Keywords: Linear programming ; Algebraic numbers ; Computational complexity ; Ellipsoid method ; Polynomial-time algorithms
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We derive a bound on the computational complexity of linear programs whose coefficients are real algebraic numbers. Key to this result is a notion of problem size that is analogous in function to the binary size of a rational-number problem. We also view the coefficients of a linear program as members of a finite algebraic extension of the rational numbers. The degree of this extension is an upper bound on the degree of any algebraic number that can occur during the course of the algorithm, and in this sense can be viewed as a supplementary measure of problem dimension. Working under an arithmetic model of computation, and making use of a tool for obtaining upper and lower bounds on polynomial functions of algebraic numbers, we derive an algorithm based on the ellipsoid method that runs in time bounded by a polynomial in the dimension, degree, and size of the linear program. Similar results hold under a rational number model of computation, given a suitable binary encoding of the problem input.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Algorithmica 6 (1991), S. 153-181 
    ISSN: 1432-0541
    Keywords: Karmarkar's algorithm ; Linear programming ; Projective algorithm ; Conical projection ; Interior methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Since Karmarkar published his algorithm for linear programming, several different interior directions have been proposed and much effort was spent on the problem transformations needed to apply these new techniques. This paper examines several search directions in a common framework that does not need any problem transformation. These directions prove to be combinations of two problem-dependent vectors, and can all be improved by a bidirectional search procedure. We conclude that there are essentially two polynomial algorithms: Karmarkar's method and the algorithm that follows a central trajectory, and they differ only in a choice of parameters (respectively lower bound and penalty multiplier).
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Algorithmica 8 (1992), S. 161-176 
    ISSN: 1432-0541
    Keywords: Parametric linear programming ; Sensitivity analysis ; Postoptimality analysis ; Linear programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We present a new definition of optimality intervals for the parametric right-hand side linear programming (parametric RHS LP) Problem ϑ(λ) = min{c t x¦Ax =b + λ¯b,x ≥ 0}. We then show that an optimality interval consists either of a breakpoint or the open interval between two consecutive breakpoints of the continuous piecewise linear convex function ϑ(λ). As a consequence, the optimality intervals form a partition of the closed interval {λ; ¦ϑ(λ)¦ 〈 ∞}. Based on these optimality intervals, we also introduce an algorithm for solving the parametric RHS LP problem which requires an LP solver as a subroutine. If a polynomial-time LP solver is used to implement this subroutine, we obtain a substantial improvement on the complexity of those parametric RHS LP instances which exhibit degeneracy. When the number of breakpoints of ϑ(λ) is polynomial in terms of the size of the parametric problem, we show that the latter can be solved in polynomial time.
    Type of Medium: Electronic Resource
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Algorithmica 11 (1994), S. 525-541 
    ISSN: 1432-0541
    Keywords: On-line algorithms ; k-Server problem ; Linear programming ; Approximation algorithms ; Paging ; Caching ; Competitive analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Weighted caching is a generalization ofpaging in which the cost to evict an item depends on the item. We study both of these problems as restrictions of the well-knownk-server problem, which involves moving servers in a graph in response to requests so as to minimize the distance traveled. We give a deterministic on-line strategy for weighted caching that, on any sequence of requests, given a cache holdingk items, incurs a cost within a factor ofk/(k−h+1) of the minimum cost possible given a cache holdingh items. The strategy generalizes “least recently used,” one of the best paging strategies in practice. The analysis is a primal-dual analysis, the first for an on-line problem, exploiting the linear programming structure of thek-server problem. We introduceloose competitiveness, motivated by Sleator and Tarjan's complaint [ST] that the standard competitive ratios for paging strategies are too high. Ak-server strategy isloosely c(k)-competitive if, for any sequence, foralmost all k, the cost incurred by the strategy withk serverseither is no more thanc(k) times the minimum costor is insignificant. We show that certain paging strategies (including “least recently used,” and “first in first out”) that arek-competitive in the standard model are looselyc(k)-competitive providedc(k)/Ink→∞ and bothk/c(k) andc(k) are nondecreasing. We show that the marking algorithm, a randomized paging strategy that is Θ(Ink)-competitive in the standard model, is looselyc(k)-competitive providedk−2 In Ink→∞ and both 2 Ink−c(k) andc(k) are nondecreasing.
    Type of Medium: Electronic Resource
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  • 5
    Electronic Resource
    Electronic Resource
    Springer
    Algorithmica 15 (1996), S. 332-350 
    ISSN: 1432-0541
    Keywords: Linear programming ; Interior-point methods ; Homotopy methods ; Predictor-corrector ; Infeasible-interior methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract A fundamental homotopy-based linear programming algorithm, which utilizes Euler-predictor and Newton-corrector steps with restarts, is formulated and investigated numerically on problems representative of linear programs that arise in practice. A rich array of refinements of this basic algorithm are possible within the homotopy framework. Such refinements are needed in any practical implementation and are discussed in detail. Implications for the design of integrated large-scale mathematical programming software are also briefly considered.
    Type of Medium: Electronic Resource
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  • 6
    Electronic Resource
    Electronic Resource
    Springer
    Algorithmica 1 (1986), S. 425-453 
    ISSN: 1432-0541
    Keywords: Linear programming ; Polynomial complexity ; Newton method
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract An algorithm is presented for solving a set of linear equations on the nonnegative orthant. This problem can be made equivalent to the maximization of a simple concave function subject to a similar set of linear equations and bounds on the variables. A Newton method can then be used which enforces a uniform lower bound which increases geometrically with the number of iterations. The basic steps are a projection operation and a simple line search. It is shown that this procedure either proves in at mostO(n 2 m 2 L) operations that there is no solution or, else, computes an exact solution in at mostO(n 3 m 2 L) operations. The linear programming problem is treated as a parametrized feasibility problem and solved in at mostO(n 3 m 2 L) operations.
    Type of Medium: Electronic Resource
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  • 7
    Electronic Resource
    Electronic Resource
    Springer
    Algorithmica 1 (1986), S. 517-527 
    ISSN: 1432-0541
    Keywords: Linear programming ; Projective method ; Box constraints
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract A specialization of the projective method for linear programming to problems with lower and upper bounds on the variables is proposed.
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  • 8
    Electronic Resource
    Electronic Resource
    Springer
    Algorithmica 1 (1986), S. 537-539 
    ISSN: 1432-0541
    Keywords: Linear programming ; Karmarkar's algorithm
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We simplify and strengthen the analysis of the improvement obtained in one step of Karmarkar's algorithm.
    Type of Medium: Electronic Resource
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  • 9
    Electronic Resource
    Electronic Resource
    Springer
    Algorithmica 1 (1986), S. 529-535 
    ISSN: 1432-0541
    Keywords: Homotopy ; Linear programming ; Interior point methods ; Nonlinear programming ; Karmarkar's method ; Quadratic regularization ; Path-following
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract In this note, we consider the solution of a linear program, using suitably adapted homotopy techniques of nonlinear programming and equation solving that move through the interior of the polytope of feasible solutions. The homotopy is defined by means of a quadratic regularizing term in an appropriate metric. We also briefly discuss algorithmic implications and connections with the affine variant of Karmarkar's method.
    Type of Medium: Electronic Resource
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  • 10
    Electronic Resource
    Electronic Resource
    Springer
    Algorithmica 9 (1993), S. 64-83 
    ISSN: 1432-0541
    Keywords: Linear programming ; Interior-point methods ; Projective methods ; Combined phase 1-phase 2
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We compare the projective methods for linear programming due to de Ghellinck and Vial, Anstreicher, Todd, and Fraley. These algorithms have the feature that they approach feasibility and optimality simultaneously, rather than requiring an initial feasible point. We compare the directions used in these methods and the lower-bound updates employed. In many cases the directions coincide and two of the lower-bound updates give the same result. It appears that Todd's direction and Fraley's lower-bound update have slight advantages, and this is borne out in limited computational testing.
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