'Recursive calls' presentation slideshows

Recursion

Recursion. Chapter 5. Chapter Objectives. To understand how to think recursively To learn how to trace a recursive method To learn how to write recursive algorithms and methods for searching arrays To learn about recursive data structures and recursive methods for a LinkedList class

1.27k views • 94 slides

Optimizing Graph Algorithms for Improved Cache Performance

900 views • 70 slides

A Hardware Processing Unit For Point Sets

A Hardware Processing Unit For Point Sets. S. Heinzle, G. Guennebaud, M. Botsch, M. Gross Graphics Hardware 2008. Motivation. Point-based graphics established Powerful algorithms Representation Processing Manipulation Rendering Decomposition Get neighborhood Operate on neighbors.

578 views • 44 slides

CSCE 411H Design and Analysis of Algorithms

CSCE 411H Design and Analysis of Algorithms. Set 3: Divide and Conquer Prof. Evdokia Nikolova* Spring 2013. * Slides adapted from Prof. Jennifer Welch. General Idea of Divide & Conquer. Take your problem and divide it up into smaller pieces Solve one or more of the smaller problems

522 views • 41 slides

Sorting

523 views • 38 slides

Recursion

Recursion. Recursion. Recursion is a technique that solves a problem by solving a smaller problem of the same type. A recursive function is a function invoking itself, either directly or indirectly. Recursion can be used as an alternative to iteration.

645 views • 36 slides

Chapter IV: Macro Processors

Overview: To study the design and implementation of macro processors. A macro represents a commonly used group of statements in the source programming language. The macro processor replaces each macro instruction with the corresponding group of source language statements. (expanding) .

727 views • 33 slides

CS61A Lecture 5

491 views • 31 slides

Programming Methods Dr Robert Harle

Programming Methods Dr Robert Harle. IA NST CS and CST Lent 2008/09 Handout 5. Blob Tracking. Our goal is to build an app that detects when something enters the visual range of a webcam Example use: Detect when someone enters your room. The Basic Process. Get an image from the webcam

485 views • 30 slides

Final Exam Review

Final Exam Review. 6 December 2010. Assignment #5. Will be due tonight @ 11:59 p.m. Take a look at my “Review Notes” if you still have no idea how to approach the problem Stars in cells with no dependencies Runtime issue

350 views • 26 slides

Defining Polynomials

Defining Polynomials. p 1 (n) is the bound on the length of an input pair <x,y> p 2 (n) is the bound on the running time of f p 3 (n) is a bound on the number of strings in S of length ? n.

348 views • 24 slides

CSC212 Data Structure - Section RS

CSC212 Data Structure - Section RS. Lecture 14 Reasoning about Recursion Instructor: Zhigang Zhu Department of Computer Science City College of New York. Outline of This Lecture. Recursive Thinking: General Form recursive calls and stopping cases Infinite Recursion runs forever

301 views • 23 slides

Introduction to Programming

Introduction to Programming. Loops. Q: What is a Loop?. A control structure that allows for a sequence of steps to be repeated a certain number of times This sequence of steps is called the body of the loop. Q: What is a Loop?. There are three basic loop structures in programming: For

409 views • 19 slides

CSC 143

CSC 143. Binary Search Trees. Costliness of contains. Review: in a binary tree, contains is O(N) contains may be a frequent operation in an application Can we do better than O(N)? Turn to list searching for inspiration... Why was binary search so much better than linear search?

359 views • 19 slides

Searching and Sorting

Searching and Sorting. Linear Search Binary Search -Reading p.671-679. Linear Search. Searching is the process of determining whether or not a given value exists in a data structure or a storage media.

762 views • 18 slides

CSE 421 Algorithms

CSE 421 Algorithms. Richard Anderson Lecture 13 Recurrences and Divide and Conquer. Divide and Conquer. Recurrence Examples. T(n) = 2 T(n/2) + cn O(n log n) T(n) = T(n/2) + cn O(n) More useful facts: log k n = log 2 n / log 2 k k log n = n log k. T(n) = aT(n/b) + f(n).

332 views • 16 slides

Recursion

Recursion. CSCE 121 J. Michael Moore. Self-Similarity. Romanesco broccoli. Self-Similarity. Fractals. Repeated self-similarity can be infinite. Koch Snowflake. Landscapes. Add randomness to repeated pattern landscapes. Fractal Landscape.

336 views • 15 slides

QuickSort

QuickSort. CSCI 3333 Data Structures. Acknowledgement. Mr. Charles Moen Dr. Wei Ding Ms. Krishani Abeysekera Dr. Michael Goodrich. Quick-sort is a randomized sorting algorithm based on the divide-and-conquer paradigm:

330 views • 13 slides

Big O

241 views • 11 slides

Find the Kth largest number

387 views • 10 slides