COMP 553: Algorithmic Game Theory Fall 2014 Yang Cai Lecture 20.

Slides:



Advertisements
Similar presentations
Theory of Computing Lecture 18 MAS 714 Hartmut Klauck.
Advertisements

6.896: Topics in Algorithmic Game Theory Lecture 8 Constantinos Daskalakis.
COMP 553: Algorithmic Game Theory Fall 2014 Yang Cai Lecture 21.
Computational Complexity in Economics Constantinos Daskalakis EECS, MIT.
6.896: Topics in Algorithmic Game Theory Lecture 11 Constantinos Daskalakis.
Department of Computer Science & Engineering
The Theory of NP-Completeness
© The McGraw-Hill Companies, Inc., Chapter 8 The Theory of NP-Completeness.
Christos alatzidis constantina galbogini.  The Complexity of Computing a Nash Equilibrium  Constantinos Daskalakis  Paul W. Goldberg  Christos H.
1 Introduction to Computability Theory Lecture15: Reductions Prof. Amos Israeli.
1 Introduction to Computability Theory Lecture12: Reductions Prof. Amos Israeli.
1 Introduction to Computability Theory Lecture13: Mapping Reductions Prof. Amos Israeli.
1 Adapted from Oded Goldreich’s course lecture notes.
CSE115/ENGR160 Discrete Mathematics 02/07/12
1 CSE 417: Algorithms and Computational Complexity Winter 2001 Lecture 23 Instructor: Paul Beame.
NP-complete examples CSC3130 Tutorial 11 Xiao Linfu Department of Computer Science & Engineering Fall 2009.
CSE 421 Algorithms Richard Anderson Lecture 27 NP Completeness.
Chapter 11: Limitations of Algorithmic Power
Toward NP-Completeness: Introduction Almost all the algorithms we studies so far were bounded by some polynomial in the size of the input, so we call them.
The Computational Complexity of Finding a Nash Equilibrium Edith Elkind, U. of Warwick.
Complexity ©D.Moshkovitz 1 Paths On the Reasonability of Finding Paths in Graphs.
4. There once lived a king, named Hagar, who had thousands of sons and daughters, still giving more births to his wives. One of his serious problems was.
Theory of Computing Lecture 19 MAS 714 Hartmut Klauck.
Efficient Partition Trees Jiri Matousek Presented By Benny Schlesinger Omer Tavori 1.
6.896: Topics in Algorithmic Game Theory Lecture 7 Constantinos Daskalakis.
The Theory of NP-Completeness 1. What is NP-completeness? Consider the circuit satisfiability problem Difficult to answer the decision problem in polynomial.
6.896: Topics in Algorithmic Game Theory Audiovisual Supplement to Lecture 5 Constantinos Daskalakis.
Chapter 11 Limitations of Algorithm Power. Lower Bounds Lower bound: an estimate on a minimum amount of work needed to solve a given problem Examples:
Computational Complexity Polynomial time O(n k ) input size n, k constant Tractable problems solvable in polynomial time(Opposite Intractable) Ex: sorting,
1 The Theory of NP-Completeness 2012/11/6 P: the class of problems which can be solved by a deterministic polynomial algorithm. NP : the class of decision.
6.853: Topics in Algorithmic Game Theory Fall 2011 Constantinos Daskalakis Lecture 11.
Lecture 22 More NPC problems
Theory of Computation, Feodor F. Dragan, Kent State University 1 NP-Completeness P: is the set of decision problems (or languages) that are solvable in.
Computational Complexity Theory Lecture 2: Reductions, NP-completeness, Cook-Levin theorem Indian Institute of Science.
Theory of Computing Lecture 17 MAS 714 Hartmut Klauck.
Cs3102: Theory of Computation Class 24: NP-Completeness Spring 2010 University of Virginia David Evans.
Approximation Algorithms
CSCI 2670 Introduction to Theory of Computing November 29, 2005.
Week 10Complexity of Algorithms1 Hard Computational Problems Some computational problems are hard Despite a numerous attempts we do not know any efficient.
6.896: Topics in Algorithmic Game Theory Lecture 6 Constantinos Daskalakis.
6.853: Topics in Algorithmic Game Theory Fall 2011 Constantinos Daskalakis Lecture 6.
1 The Theory of NP-Completeness 2 Cook ’ s Theorem (1971) Prof. Cook Toronto U. Receiving Turing Award (1982) Discussing difficult problems: worst case.
Graph Colouring L09: Oct 10. This Lecture Graph coloring is another important problem in graph theory. It also has many applications, including the famous.
6.853: Topics in Algorithmic Game Theory Fall 2011 Constantinos Daskalakis Lecture 10.
On the Complexity of Search Problems George Pierrakos Mostly based on: On the Complexity of the Parity Argument and Other Insufficient Proofs of Existence.
1 Chapter 34: NP-Completeness. 2 About this Tutorial What is NP ? How to check if a problem is in NP ? Cook-Levin Theorem Showing one of the most difficult.
 2005 SDU Lecture13 Reducibility — A methodology for proving un- decidability.
CS 3343: Analysis of Algorithms Lecture 25: P and NP Some slides courtesy of Carola Wenk.
CSCI-256 Data Structures & Algorithm Analysis Lecture Note: Some slides by Kevin Wayne. Copyright © 2005 Pearson-Addison Wesley. All rights reserved. 29.
CS6045: Advanced Algorithms NP Completeness. NP-Completeness Some problems are intractable: as they grow large, we are unable to solve them in reasonable.
NPC.
NP Completeness Piyush Kumar. Today Reductions Proving Lower Bounds revisited Decision and Optimization Problems SAT and 3-SAT P Vs NP Dealing with NP-Complete.
6.853: Topics in Algorithmic Game Theory Fall 2011 Constantinos Daskalakis Lecture 8.
NP-Completeness Note. Some illustrations are taken from (KT) Kleinberg and Tardos. Algorithm Design (DPV)Dasgupta, Papadimitriou, and Vazirani. Algorithms.
CSCI 2670 Introduction to Theory of Computing December 2, 2004.
6.853: Topics in Algorithmic Game Theory Fall 2011 Constantinos Daskalakis Lecture 9.
Network Formation Games. NFGs model distinct ways in which selfish agents might create and evaluate networks We’ll see two models: Global Connection Game.
1 The Theory of NP-Completeness 2 Review: Finding lower bound by problem transformation Problem X reduces to problem Y (X  Y ) iff X can be solved by.
More NP-Complete and NP-hard Problems
Mathematical Foundations of AI
Copyright © Cengage Learning. All rights reserved.
COMP/MATH 553: Algorithmic Game Theory
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.
NP-Completeness Yin Tat Lee
Analysis and design of algorithm
Chapter 11 Limitations of Algorithm Power
NP-Completeness Yin Tat Lee
Instructor: Aaron Roth
Presentation transcript:

COMP 553: Algorithmic Game Theory Fall 2014 Yang Cai Lecture 20

In last lecture, we showed Nash’s theorem that a Nash equilibrium exists in every game. In our proof, we used Brouwer’s fixed point theorem as a Black- box. We proceed to prove Brouwer’s Theorem using a combinatorial lemma, called Sperner’s Lemma, whose proof we also provide. In today’s lecture, we explain Brouwer’s theorem, and give an illustration of Nash’s proof.

Brouwer’ s Fixed Point Theorem

Brouwer’s fixed point theorem f Theorem: Let f : D D be a continuous function from a convex and compact subset D of the Euclidean space to itself. Then there exists an x s.t. x = f (x). N.B. All conditions in the statement of the theorem are necessary. closed and bounded D D Below we show a few examples, when D is the 2-dimensional disk.

Brouwer’s fixed point theorem fixed point

Brouwer’s fixed point theorem fixed point

Brouwer’s fixed point theorem fixed point

Nash’s Proof

Nash’s Function where:

 : [0,1] 2  [0,1] 2, continuous such that fixed points  Nash eq. Kick Dive Left Right Left1, -1-1, 1 Right-1, 1 1, -1 Visualizing Nash’s Construction Penalty Shot Game

Kick Dive Left Right Left1, -1-1, 1 Right-1, 1 1, -1 Visualizing Nash’s Construction Penalty Shot Game Pr[Right]

Kick Dive Left Right Left1, -1-1, 1 Right-1, 1 1, -1 Visualizing Nash’s Construction Penalty Shot Game Pr[Right]

Kick Dive Left Right Left1, -1-1, 1 Right-1, 1 1, -1 Visualizing Nash’s Construction Penalty Shot Game Pr[Right]

  : [0,1] 2  [0,1] 2, cont. such that fixed point  Nash eq. Kick Dive Left Right Left1, -1-1, 1 Right-1, 1 1, -1 Visualizing Nash’s Construction Penalty Shot Game Pr[Right] fixed point ½ ½ ½ ½

Sperner’s Lemma

no red no blue no yellow Lemma: Color the boundary using three colors in a legal way.

Sperner’s Lemma Lemma: Color the boundary using three colors in a legal way. No matter how the internal nodes are colored, there exists a tri-chromatic triangle. In fact an odd number of those. no red no blue no yellow

Sperner’s Lemma Lemma: Color the boundary using three colors in a legal way. No matter how the internal nodes are colored, there exists a tri-chromatic triangle. In fact an odd number of those.

Sperner’s Lemma Lemma: Color the boundary using three colors in a legal way. No matter how the internal nodes are colored, there exists a tri-chromatic triangle. In fact an odd number of those.

Proof of Sperner’s Lemma Lemma: Color the boundary using three colors in a legal way. No matter how the internal nodes are colored, there exists a tri-chromatic triangle. In fact an odd number of those. For convenience we introduce an outer boundary, that does not create new tri- chromatic triangles. Next we define a directed walk starting from the bottom-left triangle.

Transition Rule: If  red - yellow door cross it with red on your left hand. ? Space of Triangles 1 2 Proof of Sperner’s Lemma Lemma: Color the boundary using three colors in a legal way. No matter how the internal nodes are colored, there exists a tri-chromatic triangle. In fact an odd number of those.

Proof of Sperner’s Lemma ! Lemma: Color the boundary using three colors in a legal way. No matter how the internal nodes are colored, there exists a tri-chromatic triangle. In fact an odd number of those. For convenience we introduce an outer boundary, that does not create new tri- chromatic triangles. Next we define a directed walk starting from the bottom-left triangle. Starting from other triangles we do the same going forward or backward. Claim: The walk cannot exit the square, nor can it loop around itself in a rho-shape. Hence, it must stop somewhere inside. This can only happen at tri-chromatic triangle…

Proof of Brouwer’s Fixed Point Theorem We show that Sperner’s Lemma implies Brouwer’s Fixed Point Theorem. We start with the 2-dimensional Brouwer problem on the square.

2D-Brouwer on the Square Suppose  : [0,1] 2  [0,1] 2, continuous must be uniformly continuous (by the Heine-Cantor theorem)Heine-Cantor theorem

2D-Brouwer on the Square Suppose  : [0,1] 2  [0,1] 2, continuous must be uniformly continuous (by the Heine-Cantor theorem)Heine-Cantor theorem choose some and triangulate so that the diameter of cells is

2D-Brouwer on the Square Suppose  : [0,1] 2  [0,1] 2, continuous must be uniformly continuous (by the Heine-Cantor theorem)Heine-Cantor theorem color the nodes of the triangulation according to the direction of choose some and triangulate so that the diameter of cells is

D-Brouwer on the Square Suppose  : [0,1] 2  [0,1] 2, continuous must be uniformly continuous (by the Heine-Cantor theorem)Heine-Cantor theorem color the nodes of the triangulation according to the direction of tie-break at the boundary angles, so that the resulting coloring respects the boundary conditions required by Sperner’s lemma find a trichromatic triangle, guaranteed by Sperner choose some and triangulate so that the diameter of cells is

2D-Brouwer on the Square Suppose  : [0,1] 2  [0,1] 2, continuous must be uniformly continuous (by the Heine-Cantor theorem)Heine-Cantor theorem Claim: If z Y is the yellow corner of a trichromatic triangle, then

Proof of Claim Claim: If z Y is the yellow corner of a trichromatic triangle, then Proof: Let z Y, z R, z B be the yellow/red/blue corners of a trichromatic triangle. By the definition of the coloring, observe that the product of Hence: Similarly, we can show:

2D-Brouwer on the Square Suppose  : [0,1] 2  [0,1] 2, continuous must be uniformly continuous (by the Heine-Cantor theorem)Heine-Cantor theorem Claim: If z Y is the yellow corner of a trichromatic triangle, then Choosing

2D-Brouwer on the Square - pick a sequence of epsilons: - define a sequence of triangulations of diameter: - pick a trichromatic triangle in each triangulation, and call its yellow corner Claim: Finishing the proof of Brouwer’s Theorem: - by compactness, this sequence has a converging subsequence with limit point Proof: Define the function. Clearly, is continuous since is continuous and so is. It follows from continuity that But. Hence,. It follows that. Therefore,

How hard is computing a Nash Equilibrium?

NASH, BROUWER and SPERNER We informally define three computational problems: NASH: find a (appx-) Nash equilibrium in a n player game. BROUWER: find a (appx-) fixed point x for a continuous function f(). SPERNER: find a trichromatic triangle (panchromatic simplex) given a legal coloring.

Function NP (FNP) A search problem L is defined by a relation R L (x, y) such that R L (x, y)=1 iff y is a solution to x A search problem L belongs to FNP iff there exists an efficient algorithm A L (x, y) and a polynomial function p L (  ) such that (ii) if  y s.t. R L (x, y)=1   z with |z| ≤ p L (|x|) such that A L (x, z)=1 Clearly, SPERNER  FNP. (i) if A L (x, z)=1  R L (x, z)=1 A search problem is called total iff for all x there exists y such that R L (x, y) =1.

Reductions between Problems A search problem L  FNP, associated with A L (x, y) and p L, is polynomial-time reducible to another problem L’  FNP, associated with A L’ (x, y) and p L’, iff there exist efficiently computable functions f, g such that (i) x is input to L  f(x) is input to L’ A L’ (f(x), y)=1  A L (x, g(y))=1 R L’ (f(x), y)=0,  y  R L (x, y)=0,  y (ii) A search problem L is FNP-complete iff L’ is poly-time reducible to L, for all L’  FNP L  FNP e.g. SAT

Our Reductions (intuitively) NASHBROUWERSPERNER both Reductions are polynomial-time Is then SPERNER FNP-complete? - With our current notion of reduction the answer is no, because SPERNER always has a solution, while a SAT instance may not have a solution; - To attempt an answer to this question we need to update our notion of reduction. Suppose we try the following: we require that a solution to SPERNER informs us about whether the SAT instance is satisfiable or not, and provides us with a solution to the SAT instance in the ``yes’’ case;  FNP but if such a reduction existed, it could be turned into a non-deterministic algorithm for checking “no” answers to SAT: guess the solution to SPERNER; this will inform you about whether the answer to the SAT instance is “yes” or “no”, leading to … - Another approach would be to turn SPERNER into a non-total problem, e.g. by removing the boundary conditions; this way, SPERNER can be easily shown FNP- complete, but all the structure of the original problem is lost in the reduction.