http://servo.ad.wlu.edu/dokuwiki/ 2026-04-19T18:39:27+00:00 http://servo.ad.wlu.edu/dokuwiki/ http://servo.ad.wlu.edu/dokuwiki/lib/exe/fetch.php/wiki/dokuwiki-128.png text/html 2012-01-24T22:32:48+00:00 Anonymous (anonymous@undisclosed.example.com) http://servo.ad.wlu.edu/dokuwiki/doku.php/courses/cs211/winter2012/journals/shannon/entry_1_chapter_1_2.1-2?rev=1327444368&do=diff Preface Summary The preface introduced the textbook's content, outlining each chapter and the format in which content would be explained. In addition to providing practice problems, the book contains solved exercises in each chapter that step through the problem solving process. The book also has “pedagogical features” to help make it a good teaching resource. Each chapter is divided into sections: the problem, designing the algorithm and analyzing the algorithm. This helps separate differ… text/html 2012-01-24T22:27:38+00:00 Anonymous (anonymous@undisclosed.example.com) http://servo.ad.wlu.edu/dokuwiki/doku.php/courses/cs211/winter2012/journals/shannon/entry_1_jan._17_2012?rev=1327444058&do=diff Preface Summary The preface introduced the textbook's content, outlining each chapter and the format in which content would be explained. In addition to providing practice problems, the book contains solved exercises in each chapter that step through the problem solving process. The book also has “pedagogical features” to help make it a good teaching resource. Each chapter is divided into sections: the problem, designing the algorithm and analyzing the algorithm. This helps separate differ… text/html 2012-02-01T03:07:46+00:00 Anonymous (anonymous@undisclosed.example.com) http://servo.ad.wlu.edu/dokuwiki/doku.php/courses/cs211/winter2012/journals/shannon/entry_2_chapter_2.3-5?rev=1328065666&do=diff Section 2.3: Implementing the Stable Matching Algorithm using Lists and Arrays The remainder of chapter two reviewed array and lists implementations of the stable matching problem, common runtimes and priority queues and heaps. With an array, we can keep a list of n elements, where A[i] is the ith element in the list. We can access the ith element in O(1) time, we can check for the presence of an element by iterating through the array in O(n) time and if the elements are sorted we can do this… text/html 2012-01-31T22:39:55+00:00 Anonymous (anonymous@undisclosed.example.com) http://servo.ad.wlu.edu/dokuwiki/doku.php/courses/cs211/winter2012/journals/shannon/entry_3_chapter_3.1-5?rev=1328049595&do=diff Chapter three discusses the concept of graphs, which can be used to represent transportation networks, communication networks, information networks, social networks and dependency networks in the real world. Graphs consist of nodes or vertices connected by edges, and can be directed graphs if there is an asymmetrical relationship between two connected nodes, or undirected meaning there is a symmetrical relationship between the two nodes. A path in a undirected graph G = (V,E) is a sequence P o… text/html 2012-02-16T03:01:12+00:00 Anonymous (anonymous@undisclosed.example.com) http://servo.ad.wlu.edu/dokuwiki/doku.php/courses/cs211/winter2012/journals/shannon/entry_4_chapter_3.5-6?rev=1329361272&do=diff Section 3.5 This section discussed the concept of connectivity in directed graphs. Directed graphs are distinct from undirected graphs because instead of a node having a list of connected nodes, each node has two lists – one with nodes to which it has edges and one with nodes from which it has edges. We can traverse a directed graph using BFS or DFS, similar to the way we use them to search undirected graphs with the same running time: O(m+n). The main thing to remember is that these searche… text/html 2012-02-29T15:34:51+00:00 Anonymous (anonymous@undisclosed.example.com) http://servo.ad.wlu.edu/dokuwiki/doku.php/courses/cs211/winter2012/journals/shannon/entry_5_chapter_4.1-6_excluding_4.3?rev=1330529691&do=diff Chapter 4 covers greedy algorithms – algorithms that build solutions in small steps, choosing a decision at each step in order to optimize a certain criterion. 4.1 Interval Scheduling: The Greedy Algorithm stays ahead We can talk about a set of information as requests with start and end times. Subsets of these requests can be compatible I no two overlap in time, and optimal if the compatible sets are of maximum size. text/html 2012-03-06T22:14:47+00:00 Anonymous (anonymous@undisclosed.example.com) http://servo.ad.wlu.edu/dokuwiki/doku.php/courses/cs211/winter2012/journals/shannon/entry_6_chapter_4.7-8_5.1?rev=1331072087&do=diff Entry , Ch. 4.7-8, 5.1 Section 4.7 explains the concept of clustering, a situation in which a collection of objects is organized into coherent groups. For our purposes, we generally use a distance function on the objects, and say that those with a larger distance are less similar to each other, and vice versa. The book defines the concept of maximum spacing by giving an example set U of n objects. If we divide U into k groups, we say that a k-clustering of U is a partition of U into k non… text/html 2012-03-14T02:28:46+00:00 Anonymous (anonymous@undisclosed.example.com) http://servo.ad.wlu.edu/dokuwiki/doku.php/courses/cs211/winter2012/journals/shannon/entry_7_chapter_5.2-5.5?rev=1331692126&do=diff Section 5.2 This section looks at divide-and-conquer algorithms that create recursive calls on q subproblems of size n/2 and then combine the results in O(n) times. This resembles the recurrence Mergesort which has q = 2 recursive calls, but we might also see q > 2 or q=1 recursive calls. To represent the running time for these algorithms we say T(n) text/html 2012-03-27T19:36:18+00:00 Anonymous (anonymous@undisclosed.example.com) http://servo.ad.wlu.edu/dokuwiki/doku.php/courses/cs211/winter2012/journals/shannon/entry_8_chapter_6.1-4?rev=1332876978&do=diff Chapter 6: Dynamic Programming 6.1 Weighted Interval Scheduling: A Recursive Procedure In the interval scheduling problem the goal of our algorithm was to accept as large a set of non-overlapping intervals as possible. This chapter looks at intervals with certain values or weights from which we want to find a set of maximum value. If we have n requests, with each request I specifying a start time and finish time. Each interval has a value, and two intervals are compatible if they don’t overl… text/html 2012-04-03T16:13:53+00:00 Anonymous (anonymous@undisclosed.example.com) http://servo.ad.wlu.edu/dokuwiki/doku.php/courses/cs211/winter2012/journals/shannon/entry_9_chapter_7.1-2_7.5_7.7?rev=1333469633&do=diff 7.1 The maximum-flow problem and the Ford-Fulkerson algorithm We can use graphs to represent transportation networks, which have edges that carry traffic and nodes that act as “switches” passing the traffic. All the edges have capacities for how much traffic they can carry, source nodes that generate the traffic and sing nodes that absorb traffic when it arrives. The traffic is called flow in these graphs that starts from the source nodes, travels across the edges to be absorbed by the sink … text/html 2012-04-03T16:13:36+00:00 Anonymous (anonymous@undisclosed.example.com) http://servo.ad.wlu.edu/dokuwiki/doku.php/courses/cs211/winter2012/journals/shannon/home?rev=1333469616&do=diff Shannon's Journal Entry 1, Chapter 1, 2.1-2 Entry 2, Chapter 2.3-5 Entry 3, Chapter 3.1-5 Entry 4, Chapter 3.5-6 Entry 5, Chapter 4.1-6 excluding 4.3 Entry 6, Chapter 4.7-8, 5.1 Entry 7, Chapter 5.2-5.5 Entry 8, Chapter 6.1-4 Entry 9, Chapter 7.1-2, 7.5, 7.7