http://servo.ad.wlu.edu/dokuwiki/ 2026-05-11T23:26:57+00:00 http://servo.ad.wlu.edu/dokuwiki/ http://servo.ad.wlu.edu/dokuwiki/lib/exe/fetch.php/wiki/dokuwiki-128.png text/html 2018-01-30T04:21:08+00:00 Anonymous (anonymous@undisclosed.example.com) http://servo.ad.wlu.edu/dokuwiki/doku.php/courses/cs211/winter2018/journals/mccaffreyk/home/2.3?rev=1517286068&do=diff Chapter 2 Section 2.1: Computational Tractability Many of the problems we will focus on have clear conditions. Efficiency is an important component of algorithms. In addition to run-time efficiency, we must also consider concepts such as space efficiency. As we have touched on already, efficient algorithms generally lack unnecessary details. Efficiency is not simply how fast an algorithm runs. There are many components to efficiency such as scale, location and how the algorithm is run. Thus, i… text/html 2018-02-06T05:00:05+00:00 Anonymous (anonymous@undisclosed.example.com) http://servo.ad.wlu.edu/dokuwiki/doku.php/courses/cs211/winter2018/journals/mccaffreyk/home/3?rev=1517893205&do=diff Chapter 3 Section 3.1: Basic Definitions and Applications Graphs record relationships. Nodes are connected to one another by edges. In a directed graph, the order of nodes connected by edges is not interchangeable, but rather they are ordered pairs. If a graph does not specify direction, we assume that it is not directed. Examples of graphs in real life include computer networks, social networks, transportation networks, information networks like the world wide web, and dependency networks whi… text/html 2018-03-11T19:53:26+00:00 Anonymous (anonymous@undisclosed.example.com) http://servo.ad.wlu.edu/dokuwiki/doku.php/courses/cs211/winter2018/journals/mccaffreyk/home/4?rev=1520798006&do=diff Chapter 4: Greedy Algorithms Preface A greedy algorithm optimizes some criterion by taking the most optimization on a step by step basis without direct regard to future iterations. For a given problem, there can be many possible greedy algorithms. The challenge is finding greedy algorithms which are effective and proving their quality. text/html 2018-03-11T23:58:12+00:00 Anonymous (anonymous@undisclosed.example.com) http://servo.ad.wlu.edu/dokuwiki/doku.php/courses/cs211/winter2018/journals/mccaffreyk/home/5?rev=1520812692&do=diff Section 5.1: A First Recurrence: The Mergesort Algorithm In this chapter, we will be breaking down inputs and solving each part recursively. To compute the running time of algorithms that do this, we solve “recurrence relations”. The mergesort algorithm works by continually dividing the input into two parts and sorting them recursively. For instance, an algorithm may break a sequence of 8 down to two of 4 which are then broken down to 4 pairs of 2 and sorted. Next, the each set of 2s is merged … text/html 2018-03-26T04:18:18+00:00 Anonymous (anonymous@undisclosed.example.com) http://servo.ad.wlu.edu/dokuwiki/doku.php/courses/cs211/winter2018/journals/mccaffreyk/home/6?rev=1522037898&do=diff Chapter 6: Dynamic Programming Dynamic Programming is the opposite of Greedy and is similar to divide and conquer. It is a good tool for problems which do not have greedy aspects and can only produce exponential divide and conquer solutions. Dynamic Programming is the process of decomposing all information into a collection of sub-problems and then using their solutions to solve progressively larger sub-problems. Overall, it is more powerful than the other strategies we have tried so far.