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 61.
2. lappuse
... bins, an the items are packed by FFD into eight bins. Then the upper bound (2) is proven to be tight, since 11/9 · 6+6/9 = 72/9 = 8. The example is as follows: Let B1 = {1/2 + ε,1/4 + ε,1/4 − 2ε}, and B2 = {1/4 + 2ε,1/4 + 2ε,1/4 − 2ε ...
... bins, an the items are packed by FFD into eight bins. Then the upper bound (2) is proven to be tight, since 11/9 · 6+6/9 = 72/9 = 8. The example is as follows: Let B1 = {1/2 + ε,1/4 + ε,1/4 − 2ε}, and B2 = {1/4 + 2ε,1/4 + 2ε,1/4 − 2ε ...
3. lappuse
... bin is called as i-bin, if it contains exactly i items. Because all items fit in the optimal packing into OPT(I) optimal bins, follows that XD. y < OPT(I). Note that item X does not fit into any previous FFD bin, thus we get Y (B;) + X > ...
... bin is called as i-bin, if it contains exactly i items. Because all items fit in the optimal packing into OPT(I) optimal bins, follows that XD. y < OPT(I). Note that item X does not fit into any previous FFD bin, thus we get Y (B;) + X > ...
4. lappuse
... bin contains at most three items. From Lemma3we get that every optimal bin contains exactly three items, thus the number of the items is 3OPT(I), and all items are less than 1/2. Suppose that there are two consecutive bins Bi , (then j ...
... bin contains at most three items. From Lemma3we get that every optimal bin contains exactly three items, thus the number of the items is 3OPT(I), and all items are less than 1/2. Suppose that there are two consecutive bins Bi , (then j ...
5. lappuse
... bins, and follows that y2n2−1 + y2n2 + X > 1. (5) On the other hand, consider the first item in the nonincreasing order, which is cannot a second be later item than in some the (OPT(I) optimal bin, i.e. consider the largest A∗i,2 item ...
... bins, and follows that y2n2−1 + y2n2 + X > 1. (5) On the other hand, consider the first item in the nonincreasing order, which is cannot a second be later item than in some the (OPT(I) optimal bin, i.e. consider the largest A∗i,2 item ...
6. lappuse
... bin, and there will no more item be packed into this bin. This means that we get a new, appropriate counterexample. By induction we get a counterexample which meets property (i). Regarding (ii): If there exists an xk , then it is packed ...
... bin, and there will no more item be packed into this bin. This means that we get a new, appropriate counterexample. By induction we get a counterexample which meets property (i). Regarding (ii): If there exists an xk , then it is packed ...
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