Algorithms

CSc 22000–Fall 2025

 The City College of CUNY
Department of Computer Science

Instructor: Prof. Nelly Fazio
Lectures: T/Th, 11:00am–12:15pm, Shepard S-379
Office hours: Tuesdays, 12:30–1:30pm (and by appointment), NAC 8/201
Email: nfazio AT 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 28 Divide-and-Conquer. Examples. MergeSort. CLRS 2.3. Appendix A
3 Sep 2 Solving recurrence: Recursion-tree method. Examples. CLRS 4.4
4 Sep 4 More on Divide-and-Conquer: Maximum Subarray. CLRS 4.1
5 Sep 9 Solving recurrences: Substitution method. Examples. CLRS 4.3
6 Sep 11 Solving recurrences: Master method. Examples. CLRS 4.5
7 Sep 16 Sorting Algorithms: Heapsort. CLRS 6
8 Sep 18 Sorting Algorithms: Quicksort. CLRS 7
9 Sep 25 More on sorting: Lower bound and beyond. CLRS 8
10 Sep 30 More on sorting: Lower bound and beyond. CLRS 8
11 Oct 7 Balanced Search Trees: Red-Black Trees (I). Review CLRS 12
CLRS 13
12 Oct 9 Balanced Search Trees: B-Trees (I). CLRS 18
13 Oct 16 Balanced Search Trees: B-Trees (II). CLRS 18
14 Oct 21 Review  
15 Oct 23 Midterm Exam.  
16 Oct 28 Introduction to Dynamic Programming (I). CLRS 15
17 Oct 30 Dynamic Programming (II). Example: Rod Cutting. CLRS 15
18 Nov 4 Dynamic Programming (III). Example: Longest Common Subsequence. CLRS 15
19 Nov 6 Greedy Algorithms. CLRS 16
20 Nov 11 More on Greedy Algorithms. Huffman Codes. CLRS 16
21 Nov 13 Graphs. BFS and DFS. CLRS 22
22 Nov 18 Topological Sort. CLRS 22
23 Nov 20 Strongly Connected Components. CLRS 22
24 Nov 25 Minimum Spanning Trees. CLRS 23
25 Dec 2 Single-Source Shortest Paths: Bellman-Ford algorithm. CLRS 24
26 Dec 4 Single-Source Shortest Paths for DAGs + Dijkstra. CLRS 24
27 Dec 9 All-Pairs Shortest Paths: Floyd-Warshall.
Transitive closure of a graph. 		CLRS 25
28 Dec 11 Review
 
Dec 18 Final Exam, 11:00am—12:15pm

Copyright © Nelly Fazio