A natural way to measure the power of a distributed-computing model is to characterize the set of tasks that can be solved in it. the model. In general, however, the question of whether a given task can be solved in a given model is undecidable, even if we only consider the wait-free shared-memory model. In this paper, we address this question for restricted classes of models and tasks. We show that the question of whether a collection @math of objects, for various @math (the number of processes that can invoke the object) and @math (the number of distinct outputs the object returns), can be used by @math processes to solve wait-free @math -set consensus is decidable. Moreover, we provide a simple @math decision algorithm, based on a dynamic programming solution to the Knapsack optimization problem. We then present an wait-free set-consensus algorithm that, for each set of participating processes, achieves the best level of agreement that is possible to achieve using @math . Overall, this gives us a complete characterization of a read-write model defined by a collection of set-consensus objects through its .
Our algorithm computing the power of a set-consensus collection in @math steps (for a system of @math processes) is inspired by the dynamic programming solution to the Knapsack optimization problem described, e.g., in [Chap. 5] knapsack . Herlihy @cite_8 introduced the notion of of a given object type, i.e., the maximum number of processes that can solve consensus using instances of the type and read-write registers. It has been shown that @math -process consensus objects have consensus power @math . However, the corresponding consensus hierarchy is in general not robust, i.e., there exist object types, each of consensus number @math which, combined together, can be used to solve @math -process consensus @cite_17 . Besides objects of the same consensus number @math may not be equivalent in a system of more than @math processes @cite_20 .
@cite_17 @cite_20 @cite_8
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Given @math elements with non-negative integer weights @math and an integer capacity @math , we consider the counting version of the classic knapsack problem: find the number of distinct subsets whose weights add up to at most @math . We give the first deterministic, fully polynomial-time approximation scheme (FPTAS) for estimating the number of solutions to any knapsack constraint (our estimate has relative error @math ). Our algorithm is based on dynamic programming. Previously, randomized polynomial-time approximation schemes (FPRAS) were known first by Morris and Sinclair via Markov chain Monte Carlo techniques, and subsequently by Dyer via dynamic programming and rejection sampling. In addition, we present a new method for deterministic approximate counting using read-once branching programs. Our approach yields an FPTAS for several other counting problems, including counting solutions for the multidimensional knapsack problem with a constant number of constraints, the general integer knapsack problem, and the contingency tables problem with a constant number of rows. Given @math elements with nonnegative integer weights @math and an integer capacity @math , we consider the counting version of the classic knapsack problem: find the number of distinct subsets whose weights add up to at most the given capacity. We give a deterministic algorithm that estimates the number of solutions to within relative error @math in time polynomial in @math and @math (fully polynomial approximation scheme). More precisely, our algorithm takes time @math . Our algorithm is based on dynamic programming. Previously, randomized polynomial-time approximation schemes were known first by Morris and Sinclair via Markov chain Monte Carlo techniques and subsequently by Dyer via dynamic programming and rejection sampling. For all integers m ≥ 2, we construct an infinite sequence of deterministic objects of consensus number m with strictly increasing computational power. In particular, this refutes the Common2 Conjecture, which claimed that every deterministic object of consensus number 2 has a deterministic, wait-free implementation from 2-consensus objects and registers in a system with any finite number of processes. We consider the two-dimensional geometric knapsack problem defined as follows. Given a collection of rectangular axis-parallel items with weights, we want to find a maximum weight subset of the items that can be packed into a rectangular knapsack, i.e., which can be assigned positions within the knapsack such that the items are pairwise non-overlapping. The goal is to compute the optimal collection of items together with a feasible packing. The problem arises naturally in several applications, and various special cases of the problem have been studied. For the general case the best known result is a (2 + e)-approximation algorithm, while the only hardness result is NP-hardness. Our main result is a (1 + e)-approximation algorithm that runs in quasi-polynomial time, provided that the input data consists of (quasi-)polynomially bounded integers. We achieve this result in the setting with and without allowing rotation of the items. Our key technical contribution is to show the existence of a partition for the knapsack into a small number of rectangular boxes. Intuitively, this partition describes the segmentation of the knapsack in some near-optimal solution into large items, areas containing items that are high and thin, and areas containing items that are small and wide. Handling the interaction between these three types of items is a core bottleneck in the design of approximation algorithms for the problem and our partition allows to control this at only marginal cost. In particular, it is so powerful that we do not even need to round the sizes of the items, which is a canonical step in algorithms for geometric knapsack, geometric bin-packing, etc. Finally, we present new algorithms for two-dimensional knapsack and two-dimensional bin-packing under (1 + e)-resource augmentation with a substantially better running time dependence on e (although at the expense of requiring quasi-polynomial time). The objective value of our computed solutions is as good as the optimum without additional resources. Here we exploit a recent result showing the existence of certain low-complexity low weight separators for sets of non-overlapping rectangles.