Course Syllabus

CS 6363.004 Design and analysis of computer algorithms;   Spring 2019;   TR 10:00-11:15 AM;   ECSW 1.355

Class URL:

Professor Contact Information

Balaji Raghavachari;  (972) 883-2136;   [email protected];   ECSS 4.225

Office hours: Tue/Thu 2:20-3:00 PM, Wed 12:00 noon-2:00 PM, and by appointment.

Course Prerequisites, Co-requisites, and/or Other Restrictions

CS 5343 or equivalent (Data structures and algorithms): Analysis of algorithms. Stacks, queues, and trees, including B-trees. Heaps, hashing, and advanced sorting techniques. Disjoint sets and graphs.

Course Description

Topics: Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms; Sorting and searching; Graph algorithms; Computational complexity, lower bounds, NP-Completeness. Proofs of correctness.

* Running time analysis of algorithms, order notation

* Mathematical background: sums of sequences, Logs and polynomials, recurrences

* Correctness of algorithms: loop invariants, proof by induction

* Divide and conquer: merge/quick sort, binary search, median, n-bit multiplication, FFT, closest pair, convex hull

* Lower bounds, sorting lower bound

* Dynamic programming: rod cutting, matrix-chain multiplication, activity selection, knapsack, LCS, LMIS

* Greedy algorithms: activity selection, Huffman coding

* Graphs, DFS and its applications

* Minimum spanning trees: generic algorithm, implementation of Prim/Kruskal, disjoint sets

* Shortest paths: BFS, Bellman-Ford, DAG-shortest path, Dijkstra, Floyd-Warshall

* Max-flow problem: Ford-Fulkerson, Edmonds-Karp, Push/relabel, min-cost flow

* NP-completeness: introduction, reductions.

Student Learning Objectives/Outcomes

Study efficient algorithms for a number of fundamental problems, learn techniques for designing algorithms, prove correctness and analyze running times.

  1. Ability to understand asymptotic notations, recurrences, algorithm analysis
  2. Ability to understand divide and conquer algorithms
  3. Ability to understand greedy algorithms
  4. Ability to understand dynamic programming algorithms
  5. Ability to understand graph algorithms, flow networks
  6. Ability to understand NP-Completeness

Required Textbooks and Materials

Introduction to Algorithms, 3rd ed., Cormen, Leiserson, Rivest, and Stein. MIT Press.

Assignments & Academic Calendar

Homework assignments will be assigned every other week.

Mid-term Exam: Mar 14 (Thu);    Final exam: as announced by the Registrar: Thu, May 9: 11:00 AM-1:00 PM.

Grading Policy:                          

Cut-off score for grades based on weighted total: exams (80%), homework assignments (20%):

A: 87.5%

A-: 80%

B+: 75%

B: 70%

B-: 65%

C+: 60%

C: 50%

Course & Instructor Policies

See also UTD's policies at

[Version 1.2: Jan 8, 2019]