CSc 22000–Fall 2023

The City College of CUNY
Department of Computer Science

Instructor: Prof. Nelly Fazio
Lectures: T/Th, 11:00am–12:15pm, NAC 6/329
Office hours: T/Th, 12:30–1:30pm (and by appointment), SH-279
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 29 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. CLRS 2.3. Appendix A
3 Sep 5 Solving recurrence: Recursion-tree method. Examples. CLRS 4.4
4 Sep 7 More on Divide-and-Conquer: Maximum Subarray. CLRS 4.1
5 Sep 12 Solving recurrences: Substitution method. Examples. CLRS 4.3
6 Sep 14 Solving recurrences: Master method. Examples. CLRS 4.5
7 Sep 19 Sorting Algorithms: Heapsort. CLRS 6
8 Sep 21 Sorting Algorithms: Quicksort. CLRS 7
9 Sep 26 More on sorting: Lower bound and beyond. CLRS 8
10 Sep 28 More on sorting: Lower bound and beyond. CLRS 8
11 Oct 3 Balanced Search Trees: Red-Black Trees (I). Review CLRS 12
12 Oct 5 Balanced Search Trees: Red-Black Trees (II). CLRS 13
13 Oct 12 Balanced Search Trees: B-Trees (I). CLRS 18
14 Oct 17 Balanced Search Trees: B-Trees (II). CLRS 18
15 Oct 19 Review  
16 Oct 24 Midterm Exam.  
17 Oct 26 Dynamic Programming (I). Example: Rod Cutting. CLRS 15
18 Oct 31 Dynamic Programming (II). Example: Longest Common Subsequence. CLRS 15
19 Nov 2 Greedy Algorithms. CLRS 16
20 Nov 7 More on Greedy Algorithms. Huffman Codes. CLRS 16
21 Nov 9 Graphs. BFS and DFS. CLRS 22
22 Nov 14 Topological Sort. CLRS 22
23 Nov 16 Stronlgy Connected Components. CLRS 22
24 Nov 21 Minimum Spanning Trees. CLRS 23
  Nov 23 No class! Thanksgiving  
25 Nov 28 Single-Source Shortest Paths: Bellman-Ford algorithm. CLRS 24
26 Nov 30 Single-Source Shortest Paths for DAGs + Dijkstra. CLRS 24
27 Dec 5 All-Pairs Shortest Paths: Floyd-Warshall. CLRS 25
28 Dec 7 Review
Dec 14 Final Exam, 11:00am—12:15pm, NAC-4213

Copyright © Nelly Fazio