Titlebook: Approximation Algorithms for Combinatorial Optimization; Third International Klaus Jansen,Samir Khuller Conference proceedings 2000 Spring

涂掉

Maximum Dispersion and Geometric Maximum Weight Cliques have been considered before, with the best result being an approximation algorithm with performance ratio 2. For the case where . is fixed, we establish a linear-time algorithm that finds an optimal solution. For the case where . is part of the input, we present a polynomial-time approximation scheme.

largesse

Approximation Algorithms for a Capacitated Network Design Problem) where ρST is the performance ratio of any approximation algorithm for minimum Steiner tree. When all sources have the same demand value, the ratio improves to (nST +1) and in particular, to 2 when all nodes in the graph are sources.

geriatrician

https://doi.org/10.1007/978-3-663-13438-1s of greedy algorithms that use randomization. We study their limitations and prove that, with high probability, randomized greedy algorithms cannot achieve a performance ratio better than 3/2 when applied to binary trees of depth Ω(.), and 1.293 - o(1) when applied to binary trees of constant depth

chemoprevention

https://doi.org/10.1007/978-3-8349-9978-8ive ratio of any online algorithm as the ratio of the value of the objective function obtained by this algorithm to that of the best possible offline algorithm. We show that no online algorithm can have a competitive ratio greater than 1-(1/α)+ε for hard real-time scheduling with K ≥ 1 machines and

ANT

Approximation Algorithms for Combinatorial OptimizationThird International

FECT

Randomized Path Coloring on Binary Treess of greedy algorithms that use randomization. We study their limitations and prove that, with high probability, randomized greedy algorithms cannot achieve a performance ratio better than 3/2 when applied to binary trees of depth Ω(.), and 1.293 - o(1) when applied to binary trees of constant depth

LINE

Online Real-Time Preemptive Scheduling of Jobs with Deadlinesive ratio of any online algorithm as the ratio of the value of the objective function obtained by this algorithm to that of the best possible offline algorithm. We show that no online algorithm can have a competitive ratio greater than 1-(1/α)+ε for hard real-time scheduling with K ≥ 1 machines and

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https://doi.org/10.1007/978-3-8349-8456-2occur during project execution. A typical con- sequence is the underestimation of the expected project duration and cost frequently observed in practice. To cope with these phenomena, we consider schedulingmodels in which processingtimes are random but precedence and resource constraints are fixed.