Algorithms

CSc 22000–Spring 2025

 The City College of CUNY
Department of Computer Science

Instructor: Prof. Nelly Fazio
Lectures: T/Th, 11:00am–12:15pm, Marshak 410
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 Jan 28 Overview. Growth of functions. Asymptotic notation. InsertionSort. CLRS 1, 2.1, 2.2, 3
Review CLRS 10, 11
2 Jan 30 Divide-and-Conquer. Examples. MergeSort. CLRS 2.3. Appendix A
3 Feb 4 Solving recurrence: Recursion-tree method. Examples. CLRS 4.4
4 Feb 6 More on Divide-and-Conquer: Maximum Subarray. CLRS 4.1
5 Feb 11 Solving recurrences: Substitution method. Examples. CLRS 4.3
6 Feb 13 Solving recurrences: Master method. Examples. CLRS 4.5
7 Feb 20 Sorting Algorithms: Heapsort. CLRS 6
8 Feb 25 Sorting Algorithms: Quicksort. CLRS 7
9 Feb 27 More on sorting: Lower bound and beyond. CLRS 8
10 Mar 4 More on sorting: Lower bound and beyond. CLRS 8
11 Mar 11 Balanced Search Trees: Red-Black Trees (I). Review CLRS 12
CLRS 13
12 Mar 13 Balanced Search Trees: B-Trees (I). CLRS 18
13 Mar 18 Balanced Search Trees: B-Trees (II). CLRS 18
14 Mar 20 Review  
15 Mar 25 Midterm Exam.  
16 Mar 27 Introduction to Dynamic Programming (I). CLRS 15
17 Apr 1 Dynamic Programming (II). Example: Rod Cutting. CLRS 15
18 Apr 3 Dynamic Programming (III). Example: Longest Common Subsequence. CLRS 15
19 Apr 8 Greedy Algorithms. CLRS 16
20 Apr 10 More on Greedy Algorithms. Huffman Codes. CLRS 16
21 Apr 22 Graphs. BFS and DFS. CLRS 22
22 Apr 24 Topological Sort. CLRS 22
23 Apr 29 Strongly Connected Components. CLRS 22
24 May 1 Minimum Spanning Trees. CLRS 23
25 May 6 Single-Source Shortest Paths: Bellman-Ford algorithm. CLRS 24
26 May 8 Single-Source Shortest Paths for DAGs + Dijkstra. CLRS 24
27 May 13 All-Pairs Shortest Paths: Floyd-Warshall. CLRS 25
28 May 15 Review
 
May 22 Final Exam, 11:00am—12:15pm, Marshak 410

Copyright © Nelly Fazio