'Upper bounds' diaporamas de présentation

Analysis of Algorithms: Methods and Examples

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2.1 Computational Tractability

2.1 Computational Tractability. "For me, great algorithms are the poetry of computation. Just like verse, they can be terse, allusive, dense, and even mysterious. But once unlocked, they cast a brilliant new light on some aspect of computing." - Francis Sullivan. Computational Tractability.

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On-line list colouring of graphs

On-line list colouring of graphs. Xuding Zhu Zhejiang Normal University. Suppose G is a graph. A list assignment L assigns to each vertex x a set L(x) of permissible colours. An L -colouring h of G assigns to each vertex x a colour. such that for every edge x y. choice number.

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Wide-Sense Nonblocking Under New Compound Routing Strategies

Wide-Sense Nonblocking Under New Compound Routing Strategies. Junyi David Guo ( ??? ) ???? ??? 2011/06/29 Joint work with F.H. Chang. Applications. Come from the need to interconnect telephones Interconnect processors with memories Data transmission Conference calls

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On the Topology of Wireless Sensor Networks

On the Topology of Wireless Sensor Networks. Sen Yang, Xinbing Wang, Luoyi Fu Department of Electronic Engineering, Shanghai Jiao Tong University , China Email: {twood,xwang8,fly}@sjtu.edu.cn. Outline. Introduction Motivations Objectives System Models

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8.6 Partial Orderings

8.6 Partial Orderings. Definition: Let R be a relation on A. Then R is a partial order iff R is reflexive antisymmetric transitive (A, R) is called a partially ordered set or a poset . Partial Orderings.

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First Fit Coloring of Interval Graphs

First Fit Coloring of Interval Graphs William T. Trotter Georgia Institute of Technology October 14, 2005 Interval Graphs First Fit with Left End Point Order Provides Optimal Coloring Interval Graphs are Perfect ? = ? = 4 What Happens with Another Order?

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Efficiently Evaluating Order Preserving Similarity Queries over Historical Market-Basket Data

Efficiently Evaluating Order Preserving Similarity Queries over Historical Market-Basket Data. Reza Sherkat and Davood Rafiei Department of Computing Science University of Alberta Canada. Travel assistance provided by the Mary Louise Imrie Graduate Student Award. Overview. Introduction

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CHAPTER SEVEN

CHAPTER SEVEN. PORTFOLIO ANALYSIS. THE EFFICIENT SET THEOREM. THE THEOREM An investor will choose his optimal portfolio from the set of portfolios that offer maximum expected returns for varying levels of risk, and minimum risk for varying levels of returns. THE EFFICIENT SET THEOREM.

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Synthesizing parametric specifications of dynamic memory utilization in object-oriented programs

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Bart Jansen Vertex Cover Kernelization Revisited: Upper and Lower Bounds for a Refined Parameter

Bart Jansen Vertex Cover Kernelization Revisited: Upper and Lower Bounds for a Refined Parameter. Joint work with Hans Bodlaender. STACS 2011, Dortmund March 10 th , 2011. Vertex Cover. Input: Graph G, integer k Question: Does G have a vertex cover of size ? k?.

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Upper and lower bounds starter

Upper and lower bounds starter. 28 has been rounded to the nearest whole number. What is the minimum and maximum value?. Upper and lower bounds. The lower bound is 27.5 and the upper bound is 28.5

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CS276 Information Retrieval and Web Search Chris Manning and Pandu Nayak Efficient scoring

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Integer Representations and Counting in the Bit Probe Model

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Extreme probability distributions of random/fuzzy sets and p-boxes Alberto Bernardini; University of Padua, Italy Fulvi

Extreme probability distributions of random/fuzzy sets and p-boxes Alberto Bernardini; University of Padua, Italy Fulvio Tonon; University of Texas, USA. Outline. Review of Imprecise Probability Objective Set of probability distributions for: Choquet capacities Random sets Fuzzy sets

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Are lower bounds hard to prove?

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Lecture 9 Illustrations

Lecture 9 Illustrations. Lattices. Fixpoints Abstract Interpretation. Partially Ordered Set (A, ?). x ? x x ? y / y ? x ? x = y (else it is only pre-order) x ? y / y ? z ? x ? z Typical example: (A, ?), where A ? 2 U for some U. Hasse diagram. Key Terminology.

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Supremum and Infimum

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Accuracy

Accuracy. Learning Outcomes Round off to a given number of decimal places Round off to a given number of significant figures Calculate the upper and lower bounds in the addition, subtraction, multiplication and division of numbers expressed to a given degree of accuracy

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