Lecture Schedule and Notes

Current homework is available from the homework page.

Date	Topics and Lecture Notes (See below for color codes for credits for lecture notes)	Required Readings (from [CLRS] unless otherwise noted)
Tues, Aug 29	Overview of the course: Introduction to Algorithms (SS) analyzing algorithms [PS]	[CLRS 1]
Thurs, Aug 31	Introduction to Algorithms (JR) Models of Computation [PDF] [PS] (JR) Computing Fibonacci Numbers [PDF] [PS] Auxiliary notes: (LA) Introduction to Algorithms [PS] Optional notes: (SS) Algorithm Design [PS]	[CLRS 1, 2] Optional: [GKP 1.1-1.2]
Tues, Sep 5	Asymptotic Analysis: (SS) asymptotic notation [PS] Auxiliary notes: (LA) Growth of functions, summations [PS] Optional notes: (JR) Asymptotics and Recurrence Equations [PDF] [PS]	[CLRS Appendix A] Optional: [GKP 2.5]
Thurs, Sep 7	Recurrence Equations: (SS) recurrence relations [PS] Auxiliary notes: (LA) Strassen's algorithm, Master method for recurrence [PS] Optional notes: (SS) Example Problems [PS] (SS) More Example Problems [PS]	[CLRS 4.0-4.2, 4.3-4.4] Optional: [CLRS 28.1-28.2]
Tues, Sep 12	Lower Bounds on Sorting: (LA) Lower bound in decision tree model, bucket and radix sort [PS] Priority Queues: (SS) Heapsort [PS] (SS) Example heap problems [PS] Optional notes: (SS) Examples of Sorting Lower Bounds [PS] (LA) Heaps and heapsort [PS]	[CLRS 6, 9] Optional: [Baase, 3.4]
Thurs, Sep 14	Quiz over Asymptotic Notation and Recurrences
Tues, Sep 19	Selection and Radix Sort: (SS) Linear Sorting [PS] (LA) Linear time selection, median lower bound [PS] (JR) Deterministic Selection and Sorting [PDF] [PS] Optional Notes: (EU) Median and Order Statistics [PS]	[CLRS 9] Optional: [Baase, 3.4]
Thurs, Sep 21	Searching: (SS) Binary Search trees [PS] (SS) Red-Black trees [PS] (SS) Red-Black tree Rotation, Insertion, Deletion [PS] (LA) Search trees, red-black trees [PS] Optional notes: (SS) Example Red-Black tree problem [PS] (SS) Example Data Structure Problem [PS] (LA) Augmented search trees, interval trees [PS] (JR) Search Algorithms [PDF] [PS]	[CLRS 10, 12.0-12.3, 13] Optional: [CLRS 13, 14] [deBerg, 10.1]
Tues, Sep 26	Overview of Randomized and Average-Case Analysis: (EU) Probabilistic Algorithms [PS] Auxiliary notes: (JR) Probability Theory [PDF] [PS]
Thurs, Sep 28	(LA) Quicksort [PS] (SS) Introduction to Quicksort [PS] (SS) Applications of Sorting [PS] Auxiliary Notes: (Littman) Sorting [PS] (Littman) Analysis of Quicksort [PS] (JR) Randomized Algorithms for Selection and Sorting [PDF] [PS] (SS) Selection Sort [PS] (SS) Examples of Quicksort Analysis [PS] Old notes for this day: Amortized analysis, Part II: (LA) Hashing, Skip Lists [PS] Treaps, Skip Lists, Shortest Paths [PS]	[CLRS 17] Optional: [Kozen, 12]
Tues, Oct 3	Amortized analysis, Part I: (EU) Amortized Analysis [PS] (LA) Aggregate analysis, potential method, binary counter, dynamic table [PS] Optional notes: (DS) Amortized Analysis
Thurs, Oct 5	Quiz on Sorting and Searching and randomized analysis
Tues, Oct 10	FALL BREAK
Thurs, Oct 12	Graph algorithms (Graph Definitions): (EU) Graph Definitions [PS] (SS) Data structures for graphs [PS] (SS) DFS and BFS [PS]	[CLRS 22] Optional: [CLRS Appendix B.4-B.5]
Tues, Oct 17	Graph algorithms (Depth First Search I): (JR) Graph Algorithms using Depth First Search [PDF] [PS] (LA) Model, basic algorithms, DFS, BFS, topological sort, strongly connected components [PS] (SS) DFS and BFS [PS]	[CLRS 22] Optional: [CLRS Appendix B.4-B.5]
Thurs, Oct 19	Graph algorithms (Depth First Search II): (JR) Graph Algorithms using Depth First Search [PDF] [PS] (LA) Model, basic algorithms, DFS, BFS, topological sort, strongly connected components [PS] (SS) DFS and BFS [PS]	[CLRS 22] Optional: [CLRS Appendix B.4-B.5]
Tues, Oct 24	Graph algorithms (Spanning Trees): (LA) Minimum spanning trees, Union-Find [PS] (EU) Minimum Weights Spanning Tree [PS] (EU) Data Structures for Disjoint Sets [PS] Optional notes: (SS) Minimum Spanning Trees [PS]	[CLRS 23, 21.0-21.3]
Thurs, Oct 26	Graph algorithms (Breadth-First search): (JR) Breadth-First Search of Graphs [PDF] [PS] Optional notes: (EU) Matching [PS]	[CLRS 23, 21.0-21.3]
Tues, Oct 31	Graph algorithms (Shortest Paths): (LA) Shortest paths [PS] (SS) Shortest path algorthms [PS] (EU) Shortest Paths Problems [PS] (EU) Bellman-Ford Algorithm [PS] (EU) All-pairs Shortest Paths [PS]	[CLRS 24.0-24.3, 25.2]
Thurs, Nov 2	(JR) Flow Algorithms [PS] [PDF]	[CLRS 26.2-26.3]
Tues, Nov 7	Review Lecture on Graph Algorithms
Thurs, Nov 9	Exam on Amortized analysis and Graph Algorithm topics
Tues, Nov 14	External memory algorithms: (LA) Model, basic upper and lower bounds, sorting, B-tree [PS] Auxiliary Notes: (JV) The Input/Output Complexity of Sorting and Related Problems (JV) External Memory Algorithms and Data Structures: Dealing with MASSIVE Data [PS] (LA) Lower bound on external sorting (LA) External-Memory Algorithms	[CLRS 15.0-15.2]
Thurs, Nov 16	Design Paradigms: (LA) Dynamic Programming, Matrix chain multiplication [PS] (SS) Introduction to Dynamic Programming [PS] (SS) Dynamic Programming applications [PS] (EU) Dynamic Programming [PS] Auxiliary Notes: (SS) Example dynamic programming problem [PS]	[CLRS]
Tues, Nov 21	Fast Fourier Transform and Algebraic Algorithms (EU) Polynomials and the Fast Fourier Transform [PS] Auxiliary Notes: (JR) FFT and Multiplication [PDF] [PS] (JR) Polynomial Computation [PDF] [PS]	[CLRS 30]
Thurs, Nov 23	THANKSGIVING BREAK
Tues, Nov 28	Number Theory: (JR) Number Theory Algorithms [PDF] [PS]	[CLRS 31]
Thurs, Nov 30	Cryptography: (EU) Cryptography [PS]
Tues, Dec 5	Approximation Algorithms: (LA) Approximation Algorithms [PS] (EU) Approximately Correct Algorithms [PS] Auxiliary Notes: (SS) Approximation Algorithms and Cook's Theorem [PS]	[CLRS 35]
Thurs, Dec 7	Exam on Network flows, Dynamic Programming and Algebraic Algorithms
Sunday, Dec 17, 2:00 pm - 5:00 pm	PLEASE DOUBLE-CHECK THE EXAM DATE AND TIME WITH REGISTRAR	Final Exam

Color codes and initials for credits for lecture notes:

(LA) Lars Arge - graduate level lecture notes with detailed proofs (some edits by Jeff Vitter).
(HE) Herbert Edelsbrunner - graduate level notes with thorough explanations.
(JR) John H Reif - graduate level lecture notes covering many algorithm types.
(SS) Steven Skiena - graduate level lecture notes with lots of graphics.
(DS) Dan Sleator - brief lecture notes.
(EU) Eli Upfal - graduate level lecture notes with terse proofs.
(JV) Jeff Vitter

Course textbook:

[CLRS] Cormen, Leiserson, Rivest, and Stein. Introduction to Algorithms, McGraw Hill, second edition, 2001. Be sure to get the second edition!

Other references made available:

[Baase] Sara Baase. Computer Algorithms, Introduction to Design and Analysis.
[GKP] Ron Graham, Donald Knuth, and Oren Patashnik. Concrete Mathematics.
[GT] Michael. T. Goodrich and Roberto Tamassia. Data Structures and Algorithms in Java.
[Kozen] Dexter C. Kozen. The Design and Analysis of Algorithms.
[MR] Rajeev Motwani Prabhakar Raghavan. Randomized Algorithms.

Last Modified: Friday, 17-Nov-2006 14:35:02 EST