Combinatorics, Algorithms, Probabilistic and Experimental Methodologies: First International Symposium, ESCAPE 2007, Hangzhou, China, April 7-9, 2007, Revised Selected PapersBo Chen, Mike Paterson, Guochuan Zhang Springer, 2007. gada 17. sept. - 530 lappuses The refereed post-proceedings of the First International Symposium on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies are presented in this volume. The symposium provided an interdisciplinary forum for researchers to share their discoveries and approaches. The 46 full papers address large data processing problems using different methodologies from major disciplines such as computer science, combinatorics, and statistics. |
No grāmatas satura
6.–10. rezultāts no 91.
7. lappuse
... proof would be ready. Unfortunately, such FFD bins that has less weight (i.e. has some shortage) may exist. But we prove that all shortage can be covered by the reserve of the optimal bins plus the surplus of the other FFD bins, plus ...
... proof would be ready. Unfortunately, such FFD bins that has less weight (i.e. has some shortage) may exist. But we prove that all shortage can be covered by the reserve of the optimal bins plus the surplus of the other FFD bins, plus ...
8. lappuse
... Proof. Each optimal bin contains three or four items, and each FFD bin can contain at least two, and at most four items. Furthermore each bin has items with total size at most one, and each FFD has items with size more than 1−X. Using ...
... Proof. Each optimal bin contains three or four items, and each FFD bin can contain at least two, and at most four items. Furthermore each bin has items with total size at most one, and each FFD has items with size more than 1−X. Using ...
9. lappuse
... Proof. First suppose that there is not (B,M,S) optimal bin. Since there is not {(L, S),(L, S, V)} bbin, follows that by everyLitem we get 2 reserve at least, and the shortage caused by the (L,S) FFD bins are all covered. Also, since ...
... Proof. First suppose that there is not (B,M,S) optimal bin. Since there is not {(L, S),(L, S, V)} bbin, follows that by everyLitem we get 2 reserve at least, and the shortage caused by the (L,S) FFD bins are all covered. Also, since ...
10. lappuse
... Proof. The proof can be made by case analysis. The details can not fit here, but the author gladly send it to the interested reader. Thus we proved that in case 1/5 < X ≤ 1/4 our statement holds. It only remained the following case. 4.
... Proof. The proof can be made by case analysis. The details can not fit here, but the author gladly send it to the interested reader. Thus we proved that in case 1/5 < X ≤ 1/4 our statement holds. It only remained the following case. 4.
11. lappuse
... proof of FFD(L) ≤ 11/9OPT(L)+7/9. Chinese Science Bulletin 42(15) (August 1997) 2. Baker, B.S.: A new proof for the first-fit decreasing bin-packing algorithm. J. Algorithms, 49–70 (1985) 3. Coffmann, E.G., Garey Jr., M.R., Johnson ...
... proof of FFD(L) ≤ 11/9OPT(L)+7/9. Chinese Science Bulletin 42(15) (August 1997) 2. Baker, B.S.: A new proof for the first-fit decreasing bin-packing algorithm. J. Algorithms, 49–70 (1985) 3. Coffmann, E.G., Garey Jr., M.R., Johnson ...
Saturs
1 | |
12 | |
24 | |
36 | |
A Deterministic Summary Structure for Update Data Streams | 48 |
An Effective Refinement Algorithm Based on Swarm Intelligence for Graph Bipartitioning | 60 |
On the Complexity and Approximation of the MinSum and MinMax Disjoint Paths Problems | 70 |
A Digital Watermarking Scheme Based on Singular Value Decomposition | 82 |
Fast Matching Method for DNA Sequences | 271 |
AllPairs Ancestor Problems in Weighted Dags | 282 |
Streaming Algorithms for Data in Motion | 294 |
A Scheduling Problem with One Producer and the Bargaining Counterpart with Two Producers | 305 |
PhraseBased Statistical Language Modeling from Bilingual Parallel Corpus | 317 |
Optimal Commodity Distribution for a Vehicle with Fixed Capacity Under Vendor Managed Inventory | 329 |
OnLine Bin Packing with Arbitrary Release Times | 340 |
On the Complexity of the MaxEdgeColoring Problem with Its Variants | 350 |
A New t nThreshold Scheme Based on Difference Equations | 94 |
CliqueTransversal Sets in Cubic Graphs | 107 |
On the Lh kLabeling of Cocomparability Graphs | 116 |
An Approximation Algorithm for the General Mixed Packing and Covering Problem | 128 |
Extending the Hardness of RNA Secondary Structure Comparison | 140 |
On the OnLine Weighted kTaxi Problem | 152 |
Model Futility and Dynamic Boundaries with Application in Banking Default Risk Modeling | 163 |
On the Minimum RiskSum Path Problem | 175 |
Constrained Cycle Covers in Halin Graphs | 186 |
Optimal Semionline Algorithms for Scheduling with Machine Activation Cost | 198 |
A Fast Asymptotic Approximation Scheme for Bin Packing with Rejection | 209 |
Online Coupon Consumption Problem | 219 |
Application of Copula and CopulaCVaR in the Multivariate Portfolio Optimization | 231 |
Online Capacitated Interval Coloring | 243 |
Energy Efficient Heuristic Scheduling Algorithms for Multimedia Service | 255 |
Call Control and Routing in SONET Rings | 260 |
Quantitative Analysis of Multihop Wireless Networks Using a Novel Paradigm | 362 |
Inverse MinMax Spanning Tree Problem Under the Weighted SumType Hamming Distance | 375 |
Robust Optimization Model for a Class of Uncertain Linear Programs | 384 |
An Efficient Algorithm for Solving the Container Loading Problem | 396 |
A Bijective Code for kTrees with Linear Time Encoding and Decoding | 408 |
MarketBased Service Selection Framework in Grid Computing | 421 |
Informative Gene Selection and Tumor Classification by Null Space LDA for Microarray Data | 435 |
Heuristic Search for 2D NMR Alignment to Support Metabolite Identification | 447 |
A New Succinct Representation of RMQInformation and Improvements in the Enhanced Suffix Array | 459 |
Lagrangian Relaxation and Cutting Planes for the Vertex Separator Problem | 471 |
Finding Pure Nash Equilibrium of Graphical Game Via Constraints Satisfaction Approach | 483 |
A New Load Balanced Routing Algorithm for Torus Networks | 495 |
Optimal Semionline Scheduling Algorithms on aSmall Number of Machines | 504 |
Lower Bounds on Edge Searching | 516 |
Author Index | 528 |
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Combinatorics, Algorithms, Probabilistic and Experimental Methodologies ... Bo Chen Ierobežota priekšskatīšana - 2007 |
Bieži izmantoti vārdi un frāzes
alignment ALL-PAIRS approximation algorithm assigned bandwidth Berlin Heidelberg 2007 bin packing problem bins Chen color competitive ratio Computer consider constraint contains copula cost cost(A critical clique cubic graph cycle dags defined denote edge encoded Equation function given graph G greedy algorithm Hamming distance heuristic integer interval graphs k-tree labeled least Lemma linear LNCS lower bound machine method Min-Max Min-Sum minimize minimum risk-sum path Nash equilibrium nodes NP-complete NP-hard null space obtain on-line online algorithm optimal solution packing pair paper parameter partitioning phase polynomial present Proof random rejection request risk-sum path problem routing satisfies scheduling scheme searcher secret sharing sequence shortest path problem solve space Springer step strategy stream subgraph subset Theorem tree update variable vector vertex watermark weighted Zhang Eds