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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Journal of mathematical biology 25 (1987), S. 393-409 
    ISSN: 1432-1416
    Keywords: Evolution ; Evolutionary stable states ; Games theory ; Linear programming ; Convex polyhedra ; Linear complementarity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Mathematics
    Notes: Abstract The problem of finding an Evolutionary Stable Strategy (ESS) for an animal species is defined. It is shown how such strategies are a subset of the equilibrium solutions for a particular non-zero sum game. These equilibrium solutions are then shown to arise from the vertices of a particular convex polyhedron. A method of finding these equilibrium solutions through the vertices and then the ESS solutions is given. This is illustrated by a number of numerical examples taken from the literature. Finally an alternative approach based on solving a Linear Complementarity Problem is discussed.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Journal of mathematical biology 26 (1988), S. 193-197 
    ISSN: 1432-1416
    Keywords: Optimisation ; Leslie matrix ; Equilibrium ; Linear programming ; Nonlinear
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Mathematics
    Notes: Abstract The problem of optimal harvesting in equilibrium is considered in a Leslie matrix model in which both mortality and fecundity in all age-classes may be density-dependent. The conclusion is that the optimal strategy is of the two-age-class type, in common with results obtained previously for simpler models.
    Type of Medium: Electronic Resource
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  • 3
    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.
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  • 4
    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.
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  • 5
    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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  • 6
    Electronic Resource
    Electronic Resource
    Springer
    Algorithmica 6 (1991), S. 5-35 
    ISSN: 1432-0541
    Keywords: Digital circuitry ; Graph theory ; Linear programming ; Network flow ; Optimization ; Pipelining ; Propagation delay ; Retiming ; Synchronous circuitry ; Systolic circuits ; Timing analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract This paper describes a circuit transformation calledretiming in which registers are added at some points in a circuit and removed from others in such a way that the functional behavior of the circuit as a whole is preserved. We show that retiming can be used to transform a given synchronous circuit into a more efficient circuit under a variety of different cost criteria. We model a circuit as a graph in which the vertex setV is a collection of combinational logic elements and the edge setE is the set of interconnections, each of which may pass through zero or more registers. We give anO(¦V∥E¦lg¦V¦) algorithm for determining an equivalent retimed circuit with the smallest possible clock period. We show that the problem of determining an equivalent retimed circuit with minimum state (total number of registers) is polynomial-time solvable. This result yields a polynomial-time optimal solution to the problem of pipelining combinational circuitry with minimum register cost. We also give a chacterization of optimal retiming based on an efficiently solvable mixed-integer linear-programming problem.
    Type of Medium: Electronic Resource
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  • 7
    Electronic Resource
    Electronic Resource
    Springer
    Algorithmica 8 (1992), S. 1-20 
    ISSN: 1432-0541
    Keywords: Robotics ; Grasp planning ; Robot control ; Computational Geometry ; Linear programming ; Parametric searching ; Davenport-Schinzel sequences
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract In this paper we apply techniques from computational geometry to solve several problems in grasp planning and control in robotics. We consider the problem of calculating “force targets ” for a collection ofn fingers which grasp a two-dimensional object at known positions, at which the normals to the surface are also assumed to be known at least approximately. If the points at which the fingers touch the body do not allow apositive grip to be exerted (i.e., a grip in which the fingers hold the body in equilibrium by exerting friction-free forces in the directions of the corresponding inward-directed normals), it is appropriate to find the smallest coefficient of friction for which it is possible to assign a set of forces to be exerted by the fingers (so-calledfinger-force targets) which hold the object at equilibrium and such that each individual force lies within the corresponding cone of friction. We present an algorithm for this problem which runs in time0(n log2 n log logn). We also present another algorithm for preprocessing the given data so as to allow fast computation of the desired coefficient of friction for the case in which one needs to balance any given “query” external force and torque. Finally, we discuss simpler variants of our techniques which are likely to be more efficient when the problem is solved for a small number of fingers.
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  • 8
    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.
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  • 9
    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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  • 10
    ISSN: 1432-0541
    Keywords: Linear programming ; Karmarkar's algorithm ; Potential function ; Primal-dual, Modified method ; Rank-one updates
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We consider partial updating in Kojima, Mizuno, and Yoshise's primal-dual potential reduction algorithm for linear programming. We use a simple safeguard condition to control the number of updates incurred on combined primal-dual steps. Our analysis allows for unequal steplengths in the primal and dual variables, which appears to be a computationally significant factor for primal-dual methods. The safeguard we use is a primal-dual Goldstein-Armijo condition, modified to deal with the unequal primal and dual steplengths.
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