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Titlebook: Optimization Techniques; Proceedings of the 9 K. Iracki,K. Malanowski,S. Walukiewicz Conference proceedings 1980 Springer-Verlag Berlin Hei

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樓主: 生長變吼叫
51#
發(fā)表于 2025-3-30 11:18:30 | 只看該作者
Eugeniusz Toczy?owskiance through channel blacklisting. We evaluate SpeckSense on common mote hardware and show how it classifies concurrent interference under real-world settings. We also show how SpeckSense improves the performance of an existing multichannel data collection protocol by 30%.
52#
發(fā)表于 2025-3-30 14:16:10 | 只看該作者
A. ?ilinskasa lower tax base. These financial constraints as well as other risk factors were highlighted at a 2004 conference on small water systems (Ford et al. . in .) . These constraints are more severe in developing countries. For small water systems, we attempt to answer the following questions: Is a price
53#
發(fā)表于 2025-3-30 18:32:10 | 只看該作者
54#
發(fā)表于 2025-3-31 00:08:44 | 只看該作者
55#
發(fā)表于 2025-3-31 03:19:50 | 只看該作者
56#
發(fā)表于 2025-3-31 08:50:36 | 只看該作者
,Basic solutions and a ‘simplex’ method for a class of continuous linear programs,lution. This is an essential prerequisite for a simplex-like algorithm. Such an algorithm is developed by finding analogues to the reduced costs and pivot operation of the finite simplex method. The algorithm is illustrated by an application to a model of a production/inventory problem.
57#
發(fā)表于 2025-3-31 13:01:03 | 只看該作者
Factorized variable metric algorithms for unconstrained optimization,further results concerning other complex strategies incorporating factorization procedures and recently documented [11], it seems reasonable to infer that a straightforward BFS implementation is both more efficient and more robust then any other known variable metric algorithm.
58#
發(fā)表于 2025-3-31 17:17:55 | 只看該作者
59#
發(fā)表于 2025-3-31 20:05:02 | 只看該作者
60#
發(fā)表于 2025-4-1 01:42:19 | 只看該作者
Rewriting Aggregate Queries Using Functional Dependencies within the Clouda of databases, caching and query rewriting are two important ways to improve performance. This paper proposes rewriting rules for aggregate queries using semantic caching in the cloud. We have implemented our proposal in the Pig system and conducted experiments in a private cloud.
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