CSc 22000–Fall 2022

The City College of CUNY
Department of Computer Science

Instructor: Prof. Nelly Fazio
Lectures: T/Th, 11:00am–12:15pm, NAC-6112
Office hours: Mondays, 11:00am–12:00pm (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.



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.


Posted on blackboard.

Weekly Schedule (tentative)

Lecture Date Topic Readings
1 Aug 25 Overview. Growth of functions. Asymptotic notation. InsertionSort. CLRS 1, 2.1, 2.2, 3
Review CLRS 10, 11
2 Aug 30 Divide-and-Conquer. Examples. MergeSort. CLRS 2.3, Appendix A
3 Sep 1 Solving recurrence: Recursion-tree method. Examples. CLRS 4.4
4 Sep 6 More on Divide-and-Conquer: Maximum Subarray. CLRS 4.1
5 Sep 8 Solving recurrences: Substitution method. Examples. CLRS 4.3
6 Sep 13 Solving recurrences: Master method. Examples. CLRS 4.5
7 Sep 15 Sorting Algorithms: Heapsort. CLRS 6
8 Sep 20 Sorting Algorithms: Quicksort. CLRS 7
9 Sep 22 More on sorting: Lower bound and beyond.
10 Oct 6 More on sorting: Lower bound and beyond.
11 Oct 11 Balanced Search Trees: Red-Black Trees (I). Review CLRS 12
12 Oct 13 Balanced Search Trees: Red-Black Trees (II). CLRS 13
13 Oct 18 Balanced Search Trees: B-Trees (I). CLRS 18
14 Oct 20 Balanced Search Trees: B-Trees (II). CLRS 18
15 Oct 25 Review
16 Oct 27 Midterm Exam. (tentative)  
17 Nov 1 Dynamic Programming (I). Example: Rod Cutting. CLRS 15
18 Nov 3 Dynamic Programming (III). Example: Longest Common Subsequence. CLRS 15
19 Nov 8 Greedy Algorithms. CLRS 16
20 Nov 10 More on Greedy Algorithms. Huffman Codes. CLRS 16
21 Nov 15 Graphs. BFS and DFS. CLRS 22
22 Nov 17 Topological Sort. CLRS 22
23 Nov 22 Strongly Connected Components. CLRS 22
24 Nov 29 Minimum Spanning Trees. CLRS 23
25 Dec 1 Single-Source Shortest Paths: Bellman-Ford algorithm. CLRS 24
26 Dec 6 Single-Source Shortest Paths for DAGs + Dijkstra. CLRS 24
27 Dec 8 All-Pairs Shortest Paths: Floyd-Warshall. CLRS 25
28 Dec 13 Final Exam, 11:00am—12:15pm, Room NAC-6112

Copyright © Nelly Fazio