Bypassing the unique games conjecture for two geometric problems
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Bypassing the Unique Games Conjecture for two geometric problems. Yi Wu IBM Almaden Research. Based on joint work with Venkatesan Guruswami Prasad Raghavendra Rishi Saket CMU Georgia Tech IBM . Unique Games Conjecture. Unique Games Conjecture [ Khot 02]

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Bypassing the Unique Games Conjecture for two geometric problems

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Bypassing the unique games conjecture for two geometric problems

Bypassing the Unique Games Conjecture for two geometric problems

Yi Wu

IBM Almaden Research

Based on joint work with

VenkatesanGuruswami Prasad Raghavendra Rishi Saket

CMUGeorgia Tech IBM


Unique games conjecture

Unique Games Conjecture

  • Unique Games Conjecture [Khot 02]

  • For every there is an integer such that it is NP-hard to decide whether a UG instance on labels has:

    • (YES instance)

    • (NO instance)


Bypassing the unique games conjecture for two geometric problems

Implications of UGC

For a large class of optimization problems, Semidefinite Programming (SDP) gives the best polynomial time approximation.

MAX 2SAT

MAX 2LIN

MAX 2AND

Max Cut

MAX 3CSP

MultiCut

0-EXTENSION

Max 3 SAT

Multiway Cut

MAX 3SAT

Max 4 SAT

Max 2 SAT


Status of the ugc

Status of the UGC

  • Lower bound: strong SDP integrality gap instance exists. [KV05, KS09,RS09, BGHMRS]

  • Upper bound: [Arora-Barak-Steurer 11] can be solved in time .

    • The reduction from SAT (of size to prove UGC needs to have size blowup if SAT does not have sub-exponential algorithm.


Skepticism of ugc

Skepticism of UGC

  • What if UGC is false? The optimality of SDP may not hold.

    • very few result on the optimality of SDP without UGC.

  • It is not clear whether Unique Games Conjecture is a necessary assumption for all the hardness results.


Overview of our work

Overview of our work

  • For two natural geometric problems, we prove that Semidefinite Programming gives the best polynomial time approximation withoutassuming UGC.

    • same UG-hardness results known previously.


Problem 1 subspace approximation

Problem 1: Subspace approximation

  • Input: a set of points , a number Some constant

  • Algorithmic task: finding the best dimensional subspace minimize the norm of its Euclidean distance to the points.

is the Euclidean distance between and


Special case

Special case

  • Objective function:

  • least square regression.

  • : Minimum enclosing ball.

    In this work, we study the problem for


Our results

Our results

where

Let be the -th norm of a Gaussian

  • Previous result: [Deshpande-Tulsiani-Vishnoi11] :

    • UG hardnessof approximation

    • approximation by SDP.

  • Our result: NP hardness of approximation.


Problem 2 quadratic maximization

Problem 2: Quadratic Maximization

  • Input: a symmetric matrix

  • Algorithmic goal:

    Subject to for


Special case1

Special case

  • : calculating the largest eigenvalue.

  • : the Grothendieck problem on complete graph.

    In this work, we study the problem for


Previous result

Previous Result:

[Kindler-Naor-Schechtman 06] :

  • UG hardness

  • approximation by SDP.


Our result

Our Result

  • NP-hardnessof approximation.

  • approximation by SDP.

    • independently by [Naor-Schechtman]


Remarks on our results

Remarks on our results

  • While both problems have nothing to do with Gaussian, involves Gaussian Distribution in a fundamental way.

    • Gaussian Distribution also occurs fundamentally in UG hard ness proof, coincidence?

  • Evidence that SDP can be the best algorithm for optimization problems without UGC.

    • the approximation threshold is : unlikely to have a simple alternative combinatorial algorithm?

  • Our hardness reduction have size blow up matching the Arora-Barak-Steurer algorithm’s requirement.


Proof overview for subspace approximation

Proof overview forsubspace approximation


Main gadget dictator test

Main Gadget: Dictator Test

  • A instance of subspace approximation over and .

    Equivalent problem: finding

| is the distance from to subspace orthogonal to


A dictator test instance

A Dictator Test instance

  • Completeness: for every depends only on 1 coordinate (, is less than

  • Soundness: for every that depends only on a lot of coordinates, is above

If we have a dictator test instance, then it is UG-hard get better than-approximation.


A dictator test instance1

A -Dictator Test instance

  • Let be all the points on

    • (Completeness) When ,

    • (Soundness, informal proof) When

by CLT


Reduction from smooth label cover

Reduction from Smooth Label Cover

Label sets

For edge ,

satisfies if,


Smooth label cover

Smooth Label Cover

Theorem [Khot 02] : (soundness), s.t. given an instance with label sets it is NP-hard to decide,

OPT() (YES) or OPT() (NO)

where satisfies the following property,

(smoothness) the set of projections is a good hash family.


Rest of the proof

Rest of the proof

  • Composing the Smooth Label Cover with the dictator test.


Future work

Future Work

  • Other geometric problem with only UG hardness are known.

    • Kernel Clustering

    • Learning halfspacesby degree polynomials

    • Matrix Norm (SSE hardness).


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