'Size m' presentation slideshows

Dynamic programming

Dynamic programming. Dynamic programming. To begin, the word programming is used by mathematicians to describe a set of rules which must be followed to solve a problem Thus, linear programming describes sets of rules which must be solved a linear problem

1.15k views • 96 slides

Data Structures

771 views • 70 slides

Data Structures

Data Structures. Lecture 5 B-Trees. Eran Halperin and Hanoch Levy March 2014. How does a binary tree compare with k- ary tree?. Binary worse: Higher height Cost is logk Binary better: Lower width Cost is k OVERALL: BINARY BETTER! SO WHY BOTHER WITH K- ary ? .

816 views • 69 slides

Interesting Links

Interesting Links. On the Self-Similar Nature of Ethernet Traffic Will E. Leland, Walter Willinger and Daniel V. Wilson BELLCORE Murad S. Taqqu BU. Analysis and Prediction of the Dynamic Behavior of Applications, Hosts, and Networks. Overview. What is Self Similarity?

757 views • 68 slides

LING / C SC 439/539 Statistical Natural Language Processing

LING / C SC 439/539 Statistical Natural Language Processing. Numerical Python. Outline. Overview of NumPy Creating arrays Resizing arrays I ndexing and selection O perations on one array Operations on two arrays Linear algebra. Numerical Python. Module that can be imported in Python

741 views • 63 slides

Pattern Matching

Pattern Matching. A string is a sequence of characters Examples of strings: Java program HTML document DNA sequence Digitized image An alphabet S is the set of possible characters for a family of strings Example of alphabets: ASCII Unicode {0, 1} {A, C, G, T}.

740 views • 60 slides

CS634 Advanced Computer Networking Computer Science College of William and Mary

On the Self-Similar Nature of Ethernet Traffic . Presented by: Feng Yan. Will E. Leland, Walter Willinger and Daniel V. Wilson BELLCORE Murad S. Taqqu Boston University. CS634 Advanced Computer Networking Computer Science College of William and Mary. Overview.

785 views • 56 slides

Pairwise Alignments

Pairwise Alignments. Biology 224 Instructor: Tom Peavy Sept 14 &16. <PowerPoint slides based on Bioinformatics and Functional Genomics by Jonathan Pevsner> . General approach to pairwise alignment. Choose two sequences Select an algorithm that generates a score

724 views • 53 slides

Foundations of Privacy Lecture 7

517 views • 39 slides

CS38 Introduction to Algorithms

CS38 Introduction to Algorithms. Lecture 18 May 29, 2014. Outline. coping with intractibility approximation algorithms set cover TSP center selection randomness in algorithms. Optimization Problems. many hard problems (especially NP -hard) are optimization problems

490 views • 37 slides

Dynamic Memory Allocation II Nov 7, 2002

473 views • 36 slides

6.006- Introduction to Algorithms

446 views • 33 slides

321 Final Review

321 Final Review. HW9 #2 Suppose that we flip a coin until either it comes up tails twice or we have flipped it six times. What is the expected number of times we flip the coin?. HW9 #4 : a, b in R 2 R1 = {(a, b) | a>b} R2 = {(a, b) | a?b } R3 = {( a,b ) | a<b} R4 = {( a,b ) | a?b }.

416 views • 31 slides

Sorting

Sorting. Quick Sort, Shell Sort, Counting Sort, Radix Sort AND Bucket Sort. Motivation of Sorting. Sorting algorithms contain interesting and important ideas for code optimization as well as algorithm design. Merge Sort. Last time we talked about Merge Sort

571 views • 28 slides

Hashing

Hashing. Motivating Applications. Large collection of datasets Datasets are dynamic (insert, delete) Goal: efficient searching/insertion/deletion Hashing is ONLY applicable for exact-match searching. Direct Address Tables. If the keys domain is U ? Create an array T of size U

389 views • 25 slides

Lecture 8 Architecture Independent (MPI) Algorithm Design

Lecture 8 Architecture Independent (MPI) Algorithm Design. Parallel Computing Spring 2010. Traditional PRAM Algorithm vs. Architecture Independent Parallel Algorithm Design.

273 views • 12 slides

200 views • 6 slides