6.890 Algorithmic Lower Bounds: Fun with Hardness Proofs (Fall 2014)
Prof. Erik Demaine
TAs: Sarah Eisenstat, Jayson Lynch
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Overview
Need to figure out when to give up the search for efficient algorithms?
Want to know why Tetris and Mario are computationally intractable?
Love seeing the connections between problems and how they can be transformed into each other?
Like solving puzzles that can turn into publishable papers?
This class takes a practical approach to proving problems can't be solved efficiently (in polynomial time and assuming standard complexitytheoretic assumptions like P ≠ NP). We focus on reductions and techniques for proving problems are computationally hard for a variety of complexity classes. Along the way, we'll create many interesting gadgets, learn many hardness proof styles, explore the connection between games and computation, survey several important problems and complexity classes, and crush hopes and dreams (for fast optimal solutions).
The ability to show a problem is computationally hard is a valuable tool for any algorithms designer to have. Lower bounds can tell us when we need to turn to weaker goals or stronger models of computation, or to change the problem we're trying to solve. Trying to find lower bounds can help us see what makes a problem difficult or what patterns we might be able to exploit in an algorithm. The hardness perspective can help us understand what makes problems easy, or difficult to solve.
We will organize an optional problemsolving session, during which we can
jointly try to solve open problems focusing on proving problems (we think) are
computationally hard.
In the past, similar problem sessions in 6.849 and 6.851 have led to
important new results and published papers, as well as class projects.
See also the sister course to this one being taught by MohammadTaghi Hajiaghayi simultaneously at the University of Maryland.
Topics
This is an advanced class on algorithmic reduction. We will focus on techniques for proving
problems are complete with respect to various complexity classes, not on the complexity theory itself. Here is a tentative list of topics:
 NPcompleteness
 3SAT and all its variations
 3partition
 Hamiltonicity
 2D/3D geometric problems
 PSPACE, EXPTIME, EXPSPACE
 Games, Puzzles, and Computation
 Tetris
 Nintendo games: Super Mario Bros., The Legend of Zelda,
Metroid, Pokémon, ...
 Sliding blocks and Rush Hour
 Constraint Logic
 Sudoku and other Nikoli pencilandpaper games
 Chess, Go, Othello, and other board games
 Inapproximability
 PCP theorem and OPTpreserving reductions
 APXhardness (e.g., Vertex Cover)
 Setcover hardness
 Group Steiner tree
 kdense subgraph
 Label cover and Unique Games Conjecture (UGC)
 Independent set
 Fixedparameter intractability
 Parameterpreserving reductions
 W hierarchy
 Cliquehardness
 3SUMhardness (n^{2} and the Exponential Time Hypothesis)
 Counting problems (#P)
 Solution uniqueness (ASP)
 Game theory (PPAD)
 Existential theory of the reals
 Undecidability
Readings and Resources
There is no textbook for this class, but there are two recommended books,
one book chapter, and several useful websites.
 Computers and Intractability A Guide to the Theory of NPCompleteness: book
by Michael R. Garey and David S. Johnson
 Johnson's followup NPcompleteness Columns
 Games, Puzzles, & Computation:
book by Robert A. Hearn and Erik D. Demaine
— available at MIT from CRCnetBASE
 The Design of Approximation Algorithms by David Williamson and David Shmoys: chapter 16 is about inapproximability — ebook avalible from the MIT Library, draft available for free download
 Limits to Parallel Computation: PCompleteness Theory by Raymond Greenlaw, H. James Hoover, and Walter L. Ruzzo — available for free download
 Complexity Zoo
 A compendium of NP optimization problems
 Erik's 6.046 notes proving some basic NPhardness (3Dimensional Matching, Subset Sum, 4Partition)
Specifics
Lecture Time: 
Tuesdays and Thursdays at 3:30pm–5:00pm 
Lecture Room: 
MIT room
2105

First Lecture: 
Thursday, September 4, 2014 
Problem Session Time: 
57pm Thursdays 
Problem Session Room: 
32044 
Office Hours Time: 
Tuesday 57pm 
Office Hours Location: 
MIT room
34303

Professor: 
Erik Demaine,
32G680 
TAs: 
Sarah Eisenstat,
32G635 

Jayson Lynch,
32G585B 
Staff Email: 
6890staff at csail.mit.edu 
Units: 
309 
Prerequisites: 
6.046 or equivalent background in discrete mathematics and algorithms
Alternatively, permission from the instructor.
No background in computational complexity is required (though it doesn't hurt); all needed notions will be defined in this class.

Credit: 
Hlevel. This subjects qualifies as a Theoretical Computer Science concentration subject or be counted as an AUS. 
Requirements: 
Attending lectures
Problem sets
Scribe notes for 12 lectures
Written project and project presentation

Grading
There are three requirements for the class:
 Attending lectures (unless you have a
valid excuse, which you need to tell the course staff).
 Scribing one, maybe two, lectures.
Scribe notes must be typeset using LaTeX, and
are due 4 days after lecture:
Tuesday lectures are due Saturday at 11:59pm;
Thursday lectures are due Monday at 11:59pm.
A LaTeX template can be found here.
We will then send comments and possibly expect revisions.
We will send a signup email during the second week.
Listeners may also be required to scribe one lecture.
See 6.851
for examples of scribe notes.
 Periodic problem sets.
See the psets page for details.
Problems will be posted there every two to three weeks, and will not be distributed otherwise.
 Researchoriented final project (paper and presentation).
We allow theoretical, experimental, survey, Wikipedia, and art final projects.
See the project page for more details.
History
This is a brand new class. Help shape it with your feedback and
content requests!
Participating
We welcome both undergraduate and graduate students from all universities,
although officially this is a graduate class.
If you are interested in attending the class, for credit or as a listener,
please join the
6890students mailing list.