Algorithms

CSc 22000–Fall 2020

 The City College of CUNY
Department of Computer Science

Instructor: Prof. Nelly Fazio
Lectures: M/W, 2:00pm–3:15pm, Online
Office hours: Tuesdays, 2:00pm–3:00pm (and by appointment), Online
Email: fazio AT cs DOT ccny DOT cuny DOT edu [Put CSc220 in Subject line]

[ Course Description | List of Topics | Textbook | Work Load & Grading | CUNY Academic Integrity Policy | Assignments | Weekly Schedule ]

Course Description

From the course catalog: Measuring algorithmic complexity (O-Notation); searching and sorting algorithms and their complexity; tree and graph algorithms and their complexity; classes of algorithms, such as divide-and-conquer, backtracking, greedy, probabilistic, etc. Computational complexity; the classes P and NP.

Prerequisites: CSc 212, and (CSc 217 or EE 311).

Major Topics Covered in the Course

Growth of functions. Divide-and-Conquer algorithms. Master theorem. Sorting algorithms. Advanced data structures (e.g., red-black trees, B-trees, splay trees). Dynamic programming. Greedy algorithms. Graph algorithms (e.g., BFS/DFS, shortest paths, MST, max-flow). NP-completeness. Additional topics: Amortized analysis, Fibonacci heaps, number-theoretic algorithms, and basic approximation algorithms.

Textbook

Required:

Work Load & Grading

NOTE: There will be NO make-up or substitute exams

CUNY Academic Integrity Policy

Cheating will not be tolerated. If you cheat, you risk losing your position as a student in the department and the college. CUNY policy on academic integrity can be found here. Failure to understand and follow these rules will constitute cheating, and will be dealt with as per university guidelines.

Assignments

Posted on blackboard.

Weekly Schedule (tentative)

Lecture Date Topic Readings
1 Aug 26 Overview. Growth of functions. Asymptotic notation. InsertionSort. CLRS 1, 2.1, 2.2, 3
Review CLRS 10, 11
2 Aug 31 Divide-and-Conquer. Examples. MergeSort.
Solving recurrence: Recursion-tree method. Examples.		 CLRS 2.3, 4.4. Appendix A
3 Sep 2 More on Divide-and-Conquer: Maximum Subarray. Matrix multiplication.
Solving recurrences: Substitution method. Examples. 		CLRS 4.1–4.3
4 Sep 9 Solving recurrences: Master method. Examples. CLRS 4.5
5 Sep 14 Sorting Algorithms: Heapsort. CLRS 6
6 Sep 16 Sorting Algorithms: Quicksort. CLRS 7
7 Sep 21 More on sorting: Lower bound and beyond.
 CLRS 8
8 Sep 23 Balanced Search Trees: Red-Black Trees (I). Review CLRS 12
CLRS 13
9 Sep 29 Balanced Search Trees: Red-Black Trees (II). CLRS 13
10 Sep 30 Balanced Search Trees: B-Trees (I). CLRS 18
11 Oct 5 Balanced Search Trees: B-Trees (II). CLRS 18
12 Oct 7 Dynamic Programming (I). Example: Rod Cutting. CLRS 15
13 Oct 14 Dynamic Programming (II). Example: Matrix chain multiplication. CLRS 15
14 Oct 19 Dynamic Programming (III). Example: Longest Common Subsequence. CLRS 15
15 Oct 21 Midterm Exam. (tentative)  
16 Oct 26 Greedy Algorithms. CLRS 16
17 Oct 28 Greedy Algorithms. Huffman Codes. CLRS 16
18 Nov 2 Graphs. BFS and DFS. CLRS 22
19 Nov 4 Topological Sort. SCC. CLRS 22
20 Nov 9 Minimum Spanning Trees. CLRS 23
21 Nov 11 Advanced Data Structures: Fibonacci Heaps. CLRS 19
22 Nov 16 Max-Flow. CLRS 26
23 Nov 18 String Matching. CLRS 32
24 Nov 23 Single-Source Shortest Paths: Bellman-Ford algorithm. CLRS 24
25 Nov 30 Single-Source Shortest Paths for DAGs + Dijkstra. CLRS 24
26 Dec 2 All-Pairs Shortest Paths: Floyd-Warshall. CLRS 25
27 Dec 7 NP-Completeness.
CLRS 34
28 Dec 9 NP-Completeness.
CLRS 34
Dec. 16 Final Exam, 1:00-3:15pm

Copyright © Nelly Fazio