6.896: Topics in Algorithmic Game Theory Lecture 8 Constantinos Daskalakis.

Slides:



Advertisements
Similar presentations
NP-Hard Nattee Niparnan.
Advertisements

UNC Chapel Hill Lin/Foskey/Manocha Steps in DP: Step 1 Think what decision is the “last piece in the puzzle” –Where to place the outermost parentheses.
Lecture 24 MAS 714 Hartmut Klauck
Comments We consider in this topic a large class of related problems that deal with proximity of points in the plane. We will: 1.Define some proximity.
Walks, Paths and Circuits Walks, Paths and Circuits Sanjay Jain, Lecturer, School of Computing.
Midwestern State University Department of Computer Science Dr. Ranette Halverson CMPS 2433 – CHAPTER 4 GRAPHS 1.
Introduction to Graphs
Theory of Computing Lecture 18 MAS 714 Hartmut Klauck.
COMP 553: Algorithmic Game Theory Fall 2014 Yang Cai Lecture 21.
Computational Complexity in Economics Constantinos Daskalakis EECS, MIT.
Computation of Nash Equilibrium Jugal Garg Georgios Piliouras.
2/14/13CMPS 3120 Computational Geometry1 CMPS 3120: Computational Geometry Spring 2013 Planar Subdivisions and Point Location Carola Wenk Based on: Computational.
6.896: Topics in Algorithmic Game Theory Lecture 12 Constantinos Daskalakis.
6.896: Topics in Algorithmic Game Theory Lecture 11 Constantinos Daskalakis.
Christos alatzidis constantina galbogini.  The Complexity of Computing a Nash Equilibrium  Constantinos Daskalakis  Paul W. Goldberg  Christos H.
Fast FAST By Noga Alon, Daniel Lokshtanov And Saket Saurabh Presentation by Gil Einziger.
COMP 553: Algorithmic Game Theory Fall 2014 Yang Cai Lecture 20.
Steps in DP: Step 1 Think what decision is the “last piece in the puzzle” –Where to place the outermost parentheses in a matrix chain multiplication (A.
Computability and Complexity 23-1 Computability and Complexity Andrei Bulatov Search and Optimization.
Approximation Algorithms: Combinatorial Approaches Lecture 13: March 2.
1 Perfect Matchings in Bipartite Graphs An undirected graph G=(U  V,E) is bipartite if U  V=  and E  U  V. A 1-1 and onto function f:U  V is a perfect.
Definition Dual Graph G* of a Plane Graph:
Pure Nash Equilibria: Complete Characterization of Hard and Easy Graphical Games Albert Xin Jiang U. of British Columbia MohammadAli Safari Sharif U. of.
Lecture 6: Point Location Computational Geometry Prof. Dr. Th. Ottmann 1 Point Location 1.Trapezoidal decomposition. 2.A search structure. 3.Randomized,
The Computational Complexity of Finding a Nash Equilibrium Edith Elkind, U. of Warwick.
MATH 310, FALL 2003 (Combinatorial Problem Solving) Lecture 6, Friday, September 12.
Theory of Computing Lecture 19 MAS 714 Hartmut Klauck.
6.896: Topics in Algorithmic Game Theory Lecture 7 Constantinos Daskalakis.
9.2 Graph Terminology and Special Types Graphs
CS355 - Theory of Computation Lecture 2: Mathematical Preliminaries.
 Jim has six children.  Chris fights with Bob,Faye, and Eve all the time; Eve fights (besides with Chris) with Al and Di all the time; and Al and Bob.
6.896: Topics in Algorithmic Game Theory Audiovisual Supplement to Lecture 5 Constantinos Daskalakis.
UNC Chapel Hill M. C. Lin Point Location Reading: Chapter 6 of the Textbook Driving Applications –Knowing Where You Are in GIS Related Applications –Triangulation.
Computational Complexity Polynomial time O(n k ) input size n, k constant Tractable problems solvable in polynomial time(Opposite Intractable) Ex: sorting,
Nattee Niparnan. Easy & Hard Problem What is “difficulty” of problem? Difficult for computer scientist to derive algorithm for the problem? Difficult.
6.853: Topics in Algorithmic Game Theory Fall 2011 Constantinos Daskalakis Lecture 11.
Introduction to Graphs. Introduction Graphs are a generalization of trees –Nodes or verticies –Edges or arcs Two kinds of graphs –Directed –Undirected.
6.896: Topics in Algorithmic Game Theory Lecture 6 Constantinos Daskalakis.
6.896: Topics in Algorithmic Game Theory Lecture 10 Constantinos Daskalakis.
6.853: Topics in Algorithmic Game Theory Fall 2011 Constantinos Daskalakis Lecture 6.
6.853: Topics in Algorithmic Game Theory Fall 2011 Constantinos Daskalakis Lecture 10.
Ásbjörn H Kristbjörnsson1 The complexity of Finding Nash Equilibria Ásbjörn H Kristbjörnsson Algorithms, Logic and Complexity.
1 12/2/2015 MATH 224 – Discrete Mathematics Formally a graph is just a collection of unordered or ordered pairs, where for example, if {a,b} G if a, b.
Lecture 10: Graph-Path-Circuit
UNC Chapel Hill M. C. Lin Delaunay Triangulations Reading: Chapter 9 of the Textbook Driving Applications –Height Interpolation –Constrained Triangulation.
CSE 421 Algorithms Richard Anderson Winter 2009 Lecture 5.
NP-completeness NP-complete problems. Homework Vertex Cover Instance. A graph G and an integer k. Question. Is there a vertex cover of cardinality k?
NPC.
6.853: Topics in Algorithmic Game Theory Fall 2011 Constantinos Daskalakis Lecture 8.
Chapter 9: Graphs.
6.853: Topics in Algorithmic Game Theory Fall 2011 Constantinos Daskalakis Lecture 9.
6.853: Topics in Algorithmic Game Theory Fall 2011 Constantinos Daskalakis Lecture 12.
The NP class. NP-completeness Lecture2. The NP-class The NP class is a class that contains all the problems that can be decided by a Non-Deterministic.
Lecture 20. Graphs and network models 1. Recap Binary search tree is a special binary tree which is designed to make the search of elements or keys in.
Relations and Graphs Relations and Graphs Sanjay Jain, Lecturer, School of Computing.
GRAPHS Lecture 16 CS2110 Fall 2017.
COMP/MATH 553: Algorithmic Game Theory
Perfect Matchings in Bipartite Graphs
COMP 553: Algorithmic Game Theory
Historical Note von Neumann’s original proof (1928) used Brouwer’s fixed point theorem. Together with Danzig in 1947 they realized the above connection.
Computability and Complexity
3.3 Applications of Maximum Flow and Minimum Cut
Enumerating Distances Using Spanners of Bounded Degree
Richard Anderson Lecture 25 NP-Completeness
Chapter 11 Limitations of Algorithm Power
5.4 T-joins and Postman Problems
V12 Menger’s theorem Borrowing terminology from operations research
Computing a Nash Equilibrium of a Congestion Game: PLS-completeness
GRAPHS Lecture 17 CS2110 Spring 2018.
Concepts of Computation
Presentation transcript:

6.896: Topics in Algorithmic Game Theory Lecture 8 Constantinos Daskalakis

2 point Exercise 5. NASH  BROUWER (cont.): - Final Point: We defined BROUWER for functions in the hypercube. But Nash’s function is defined on the product of simplices. Hence, to properly reduce NASH to BROUWER we first embed the product of simplices in a hypercube, then extend Nash’s function to points outside the product of simplices in a way that does not introduce approximate fixed points that do not correspond to approximate fixed points of Nash’s function.

Last Time…

The PPAD Class [Papadimitriou ’94] Suppose that an exponentially large graph with vertex set {0,1} n is defined by two circuits: P N node id END OF THE LINE: Given P and N: If 0 n is an unbalanced node, find another unbalanced node. Otherwise say “yes”. PPAD ={ Search problems in FNP reducible to END OF THE LINE} possible previous possible next “A directed graph with an unbalanced node (indegree  outdegree) must have another unbalanced node”

{0,1} n... 0n0n The Directed Graph = solution

Other Combinatorial Arguments of Existence

four arguments of existence “If a graph has a node of odd degree, then it must have another.” PPA “Every directed acyclic graph must have a sink.” PLS “If a function maps n elements to n-1 elements, then there is a collision.” PPP “If a directed graph has an unbalanced node it must have another.” PPAD

The Class PPA [Papadimitriou ’94] Suppose that an exponentially large graph with vertex set {0,1} n is defined by one circuit: C node id { node id 1, node id 2 } ODD DEGREE NODE: Given C: If 0 n has odd degree, find another node with odd degree. Otherwise say “yes”. PPA = { Search problems in FNP reducible to ODD DEGREE NODE } possible neighbors “If a graph has a node of odd degree, then it must have another.”

{0,1} n... 0n0n The Undirected Graph = solution

The Class PLS [JPY ’89] Suppose that a DAG with vertex set {0,1} n is defined by two circuits: C node id {node id 1, …, node id k } FIND SINK: Given C, F: Find x s.t. F(x) ≥ F(y), for all y  C(x). PLS = { Search problems in FNP reducible to FIND SINK } F node id “Every DAG has a sink.”

The DAG {0,1} n = solution

The Class PPP [Papadimitriou ’94] Suppose that an exponentially large graph with vertex set {0,1} n is defined by one circuit: C node id COLLISION: Given C: Find x s.t. C( x )= 0 n ; or find x ≠ y s.t. C(x)=C(y). PPP = { Search problems in FNP reducible to COLLISION } “If a function maps n elements to n-1 elements, then there is a collision.”

1 point

Hardness Results

Inclusions we have already established: Our next goal:

The PLAN... 0n0n Generic PPAD Embed PPAD graph in [0,1] 3 3D-SPERNER p.w. linear BROUWER multi-player NASH 4-player NASH 3-player NASH 2-player NASH [Pap ’94] [DGP ’05] [DP ’05] [CD’05] [CD’06] DGP = Daskalakis, Goldberg, Papadimitriou CD = Chen, Deng

This Lecture... 0n0n Generic PPAD Embed PPAD graph in [0,1] 3 3D-SPERNER p.w. linear BROUWER multi-player NASH 4-player NASH 3-player NASH 2-player NASH [Pap ’94] [DGP ’05] [DP ’05] [CD’05] [CD’06] DGP = Daskalakis, Goldberg, Papadimitriou CD = Chen, Deng

First Step... 0n0n Generic PPAD Embed PPAD graph in [0,1] 3 our goal is to identify a piecewise linear, single dimensional subset of the cube, corresponding to the PPAD graph; we call this subset L

Non-Isolated Nodes map to pairs of segments... 0n0n Generic PPAD Non-Isolated Node pair of segments main auxiliary

... 0n0n Generic PPAD pair of segments also, add an orthonormal path connecting the end of main segment and beginning of auxiliary segment breakpoints used: Non-Isolated Nodes map to pairs of segments Non-Isolated Node

Edges map to orthonormal paths... 0n0n Generic PPAD orthonormal path connecting the end of the auxiliary segment of u with beginning of main segment of v Edge between and breakpoints used:

Exceptionally 0 n is closer to the boundary…... 0n0n Generic PPAD This is not necessary for the embedding of the PPAD graph, but will be useful later in the definition of the Sperner instance…

Finishing the Embedding... 0n0n Generic PPAD Claim 1: Two points p, p’ of L are closer than 3  2 -m in Euclidean distance only if they are connected by a part of L that has length 8  2 -m or less. Call L the orthonormal line defined by the above construction. Claim 2: Given the circuits P, N of the END OF THE LINE instance, and a point x in the cube, we can decide in polynomial time if x belongs to L. Claim 3:

Reducing to 3-d Sperner Instead of coloring vertices of the triangulation (the points of the cube whose coordinates are integer multiples of 2 -m ), color the centers of the cubelets; i.e. work with the dual graph. 3-d SPERNER

Boundary Coloring legal coloring for the dual graph (on the centers of cubelets) N.B.: this coloring is not the envelope coloring we used earlier; also color names are permuted

Coloring of the Rest Rest of the coloring:

Coloring around L  two out of four cubelets are colored 3, one is colored 1 and the other is colored 2

The Beginning of L at 0 n notice that given the coloring of the cubelets around the beginning of L (on the left), there is no point of the subdivision in the proximity of these cubelets surrounded by all four colors…

Color Twisting - in the figure on the left, the arrow points to the direction in which the two cubelets colored 3 lie out of the four cubelets around L which two are colored with color 3 ? IMPORTANT directionality issue: the picture on the left shows the evolution of the location of the pair of colored 3 cubelets along the subset of L corresponding to an edge (u, v) of the PPAD graph… - observe also the way the twists of L affect the location of these cubelets with respect to L at the main segment corresponding to u the pair of cubelets lies above L, while at the main segment corresponding to v they lie below L

Color Twisting the flip in the location of the cubelets makes it impossible to locally decide where the colored 3 cubelets should lie! to resolve this we assume that all edges (u,v) of the PPAD graph join an odd u (as a binary number) with an even v (as a binary number) or vice versa for even u’s we place the pair of 3-colored cubelets below the main segment of u, while for odd u’s we place it above the main segment Claim1: This is W.L.O.G. convention agrees with coloring around main segment of 0 n

Proof of Claim of Previous Slide - Duplicate the vertices of the PPAD graph - If node u is non-isolated include an edge from the 0 to the 1 copy non-isolated - Edges connect the 1-copy of a node to the 0-copy of its out-neighbor

Finishing the Reduction Claim 1: A point in the cube is panchromatic in the described coloring iff it is: - an endpoint u 2 ’ of a sink vertex u of the PPAD graph, or - an endpoint u 1 of a source vertex u ≠0 n of the PPAD graph. A point in the cube is panchromatic iff it is the corner of some cubelet (i.e. it belongs to the subdivision of mutliples of 2 -m ), and all colors are present in the cubelets containing this point. Claim 2: Given the description P, N of the PPAD graph, there is a polynomial- size circuit computing the coloring of every cubelet.