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  • Articles  (18)
  • Linear programming  (18)
  • 2005-2009
  • 1990-1994  (18)
  • 1994  (18)
  • Mathematics  (18)
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  • Energy, Environment Protection, Nuclear Power Engineering
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  • Articles  (18)
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  • 2005-2009
  • 1990-1994  (18)
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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 67 (1994), S. 109-119 
    ISSN: 1436-4646
    Keywords: Polynomial-time ; Linear programming ; Primal-dual ; Interior-point algorithm
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Kojima, Megiddo, and Mizuno investigate an infeasible-interior-point algorithm for solving a primal—dual pair of linear programming problems and they demonstrate its global convergence. Their algorithm finds approximate optimal solutions of the pair if both problems have interior points, and they detect infeasibility when the sequence of iterates diverges. Zhang proves polynomial-time convergence of an infeasible-interior-point algorithm under the assumption that both primal and dual problems have feasible points. In this paper, we show that a modification of the Kojima—Megiddo—Mizuno algorithm “solves” the pair of problems in polynomial time without assuming the existence of the LP solution. Furthermore, we develop anO(nL)-iteration complexity result for a variant of the algorithm.
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  • 2
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    Electronic Resource
    Springer
    Mathematical programming 64 (1994), S. 1-16 
    ISSN: 1436-4646
    Keywords: Random walks ; Totally unimodular matrices ; Uniform generation ; Linear programming ; Diameter of polytopes
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We discuss the application of random walks to generating a random basis of a totally unimodular matrix and to solving a linear program with such a constraint matrix. We also derive polynomial upper bounds on the combinatorial diameter of an associated polyhedron.
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  • 3
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    Springer
    Mathematical programming 64 (1994), S. 17-51 
    ISSN: 1436-4646
    Keywords: Factorization ; Linear programming ; Generalized upper bounds ; Pure networks ; Generalized networks
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Factorization of linear programming (LP) models enables a large portion of the LP tableau to be represented implicitly and generated from the remaining explicit part. Dynamic factorization admits algebraic elements which change in dimension during the course of solution. A unifying mathematical framework for dynamic row factorization is presented with three algorithms which derive from different LP model row structures: generalized upper bound rows, pure network rows, and generalized network rows. Each of these structures is a generalization of its predecessors, and each corresponding algorithm exhibits just enough additional richness to accommodate the structure at hand within the unified framework. Implementation and computational results are presented for a variety of real-world models. These results suggest that each of these algorithms is superior to the traditional, non-factorized approach, with the degree of improvement depending upon the size and quality of the row factorization identified.
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  • 4
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    Springer
    Mathematical programming 65 (1994), S. 217-245 
    ISSN: 1436-4646
    Keywords: Linear programming ; Semi-infinite programming ; Interior-point methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract In order to study the behavior of interior-point methods on very large-scale linear programming problems, we consider the application of such methods to continuous semi-infinite linear programming problems in both primal and dual form. By considering different discretizations of such problems we are led to a certain invariance property for (finite-dimensional) interior-point methods. We find that while many methods are invariant, several, including all those with the currently best complexity bound, are not. We then devise natural extensions of invariant methods to the semi-infinite case. Our motivation comes from our belief that for a method to work well on large-scale linear programming problems, it should be effective on fine discretizations of a semi-infinite problem and it should have a natural extension to the limiting semi-infinite case.
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  • 5
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    Mathematical programming 66 (1994), S. 103-122 
    ISSN: 1436-4646
    Keywords: 42B05 ; 62A99 ; Maximum entropy ; Linear programming ; Inverse problems ; Superresolution
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract In this paper, we give two different results. We propose new methods to solve classical optimization problems in linear programming. We also obtain precise quantitative results for the superresolution phenomenon, as observed earlier by practical searchers on specific algorithms. The common background of our work is the generalized moment problem, which is known to be connected with linear programming and superresolution. We describe the Maximum Entropy Method on the Mean that provides solution to the problem and leads to computational criteria to decide the existence of solutions or not.
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  • 6
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    Springer
    Mathematical programming 66 (1994), S. 361-377 
    ISSN: 1436-4646
    Keywords: Linear programming ; Infeasible-interior-point methods ; Superlinear convergence
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract At present the interior-point methods of choice are primal—dual infeasible-interior-point methods, where the iterates are kept positive, but allowed to be infeasible. In practice, these methods have demonstrated superior computational performance. From a theoretical point of view, however, they have not been as thoroughly studied as their counterparts — feasible-interior-point methods, where the iterates are required to be strictly feasible. Recently, Kojima et al., Zhang, Mizuno and Potra studied the global convergence of algorithms in the primal—dual infeasible-interior-point framework. In this paper, we continue to study this framework, and in particular we study the local convergence properties of algorithms in this framework. We construct parameter selections that lead toQ-superlinear convergence for a merit function andR-superlinear convergence for the iteration sequence, both at rate 1 +τ whereτ can be arbitrarily close to one.
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  • 7
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    Mathematical programming 65 (1994), S. 347-363 
    ISSN: 1436-4646
    Keywords: Linear programming ; (Weighted) central paths ; Limiting behavior on central paths ; Local convergence rates of interior point algorithms ; Primary: 90C05 ; Secondary: 90C33
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We study the limiting behavior of the weighted central paths{(x(μ), s(μ))} μ 〉 0 in linear programming at bothμ = 0 andμ = ∞. We establish the existence of a partition (B ∞,N ∞) of the index set { 1, ⋯,n } such thatx i(μ) → ∞ ands j (μ) → ∞ asμ → ∞ fori ∈ B ∞, andj ∈ N ∞, andx N∞ (μ),s B∞ (μ) converge to weighted analytic centers of certain polytopes. For allk ⩾ 1, we show that thekth order derivativesx (k) (μ) ands (k) (μ) converge whenμ → 0 andμ → ∞. Consequently, the derivatives of each order are bounded in the interval (0, ∞). We calculate the limiting derivatives explicitly, and establish the surprising result that all higher order derivatives (k ⩾ 2) converge to zero whenμ → ∞.
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  • 8
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    Mathematical programming 67 (1994), S. 169-187 
    ISSN: 1436-4646
    Keywords: 90C05 ; 90C25 ; 90C31 ; 49M30 ; Linear programming ; Exponential penalty ; Optimal trajectory ; Asymptotic expansion ; Interior point methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We consider the linear program min{c′x: Ax⩽b} and the associated exponential penalty functionf r(x) = c′x + rΣexp[(A ix − bi)/r]. Forr close to 0, the unconstrained minimizerx(r) off r admits an asymptotic expansion of the formx(r) = x * + rd* + η(r) wherex * is a particular optimal solution of the linear program and the error termη(r) has an exponentially fast decay. Using duality theory we exhibit an associated dual trajectoryλ(r) which converges exponentially fast to a particular dual optimal solution. These results are completed by an asymptotic analysis whenr tends to ∞: the primal trajectory has an asymptotic ray and the dual trajectory converges to an interior dual feasible solution.
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  • 9
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    Mathematical programming 67 (1994), S. 383-406 
    ISSN: 1436-4646
    Keywords: 90C05 ; Linear programming ; Primal—dual ; Polynomial complexity ; Infeasible ; Interior-point ; Exterior-point
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract A predictor—corrector method for solving linear programs from infeasible starting points is analyzed. The method is quadratically convergent and can be combined with Ye's finite termination scheme under very general assumptions. If the starting points are large enough then the algorithm hasO(nL) iteration complexity. If the ratio between feasibility and optimality at the starting points is small enough then the algorithm has O( $$\sqrt {n L} $$ ) iteration complexity. For feasible starting points the algorithm reduces to the Mizuno—Todd—Ye predictor—corrector method.
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  • 10
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    Mathematical programming 66 (1994), S. 145-159 
    ISSN: 1436-4646
    Keywords: Linear programming ; Interior point methods ; Primal—dual algorithms ; Potential function
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We start with a study of the primal—dual affine-scaling algorithms for linear programs. Using ideas from Kojima et al., Mizuno and Nagasawa, and new potential functions we establish a framework for primal—dual algorithms that keep a potential function value fixed. We show that if the potential function used in the algorithm is compatible with a corresponding neighborhood of the central path then the convergence proofs simplify greatly. Our algorithms have the property that all the iterates can be kept in a neighborhood of the central path without using any centering in the search directions.
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  • 11
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    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.
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  • 12
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    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.
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  • 13
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    Mathematical methods of operations research 40 (1994), S. 91-108 
    ISSN: 1432-5217
    Keywords: Markov decision processes ; countable state space ; Linear programming ; duality
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract We present an Linear Programming formulation of MDPs with countable state and action spaces and no unichain assumption. This is an extension of the Hordijk and Kallenberg (1979) formulation in finite state and action spaces. We provide sufficient conditions for both existence of optimal solutions to the primal LP program and absence of duality gap. Then, existence of a (possibly randomized) average optimal policy is also guaranteed. Existence of a stationary average optimal deterministic policy is also investigated.
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  • 14
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    Journal of optimization theory and applications 81 (1994), S. 35-52 
    ISSN: 1573-2878
    Keywords: Linear programming ; optimal value functions ; redundancy in linear programming ; convex hull problem ; data envelopment analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract In 1967, Wets and Witzgall (Ref. 1) made, in passing, a connection between frames of polyhedral cones and redundancy in linear programming. The present work elaborates and formalizes the theoretical details needed to establish this relation. We study the properties of optimal value functions in order to derive the correspondence between problems in redundancy and the frame of a polyhedral cone. The insights obtained lead to schemes to improve the efficiency of procedures to detect redundancy in the areas of linear programming, stochastic programming, and computational geometry.
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  • 15
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    Journal of global optimization 4 (1994), S. 89-109 
    ISSN: 1573-2916
    Keywords: Linear programming ; simplex method ; c-programming ; composite functions ; global optimization ; dc problems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract In this paper we give a brief account of the important role that the conventional simplex method of linear programming can play in global optimization, focusing on its collaboration with composite concave programming techniques. In particular, we demonstrate how rich and powerful the c-programming format is in cases where its parametric problem is a standard linear programming problem.
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  • 16
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    Journal of optimization theory and applications 80 (1994), S. 161-173 
    ISSN: 1573-2878
    Keywords: Linear programming ; interior point methods ; containing ellipsoids ; optimal basic and nonbasic variables
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Ellipsoids that contain all optimal dual slack solutions and those that contain all optimal primal solutions and that are independent of the algorithm used are derived. Based upon these ellipsoids, two criteria each for detecting optimal basic and nonbasic variables prior to optimality in interior-point methods are obtained. Using these results, we then derive a sufficient condition for a linear program to be feasible.
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  • 17
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    Journal of optimization theory and applications 82 (1994), S. 405-413 
    ISSN: 1573-2878
    Keywords: Linear programming ; interior-point methods ; Karmarkar's method ; log-barrier function ; rank-one techniques ; computational complexity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This short paper presents a primal interior-point method for linear programming. The method can be viewed as a modification of the methods developed in Refs. 1–6. In each iteration, it computes an approximately projected Newton direction and needsO(n 2.5) arithmetic operations to make the log-barrier function significantly decrease. It requires $$O(\sqrt {nL} )$$ iterations, so that the total complexity isO(n 3 L).
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  • 18
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    Journal of optimization theory and applications 83 (1994), S. 1-26 
    ISSN: 1573-2878
    Keywords: Linear programming ; primal-dual interior point methods ; logarithmic barrier function ; polynomial algorithms
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract In this paper, we deal with primal-dual interior point methods for solving the linear programming problem. We present a short-step and a long-step path-following primal-dual method and derive polynomial-time bounds for both methods. The iteration bounds are as usual in the existing literature, namely $$O(\sqrt n L)$$ iterations for the short-step variant andO(nL) for the long-step variant. In the analysis of both variants, we use a new proximity measure, which is closely related to the Euclidean norm of the scaled search direction vectors. The analysis of the long-step method depends strongly on the fact that the usual search directions form a descent direction for the so-called primal-dual logarithmic barrier function.
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