# Approximating the Optimum Efficient Algorithms and their Limits

Shared by:
Categories
Tags
-
Stats
views:
2
posted:
2/19/2013
language:
Unknown
pages:
71
Document Sample

```							Approximating NP-hard Problems
Efficient Algorithms and their Limits

University of Washington
Seattle
Combinatorial Optimization
Problems
Set Cover            Max 3 SAT Steiner Tree
Vertex x2  x3 )(x2  x3 MultiCut  x5 )(x5  x4  x1 )
( x1  Cover            x5 )(x2  x3

Max 3 satisfies the Cover
Find an assignment thatSAT
Max Cut number of clauses.
maximum                         Label

Max Di Cut        Sparsest Cut
Multiway Cut

Max 2 SAT           Metric TSP          Max 4 SAT
Approximation Algorithms
An algorithm A isa solution that is for
Can we find an α-approximation say
a problem if for every instance I,
half as good as optimum?
A(I) ≥ α ∙ OPT(I)

--Vast Literature--
The Tools
Till 1994,
A majority of approximation algorithms directly or
indirectly relied on Linear Programming.

In 1994,
Semidefinite Programming based
algorithm for Max Cut
[Goemans-Williamson]

Semidefinite Programming - A generalization of Linear
Programming.

Semidefinite Programming is the one of the most powerful
tools in approximation algorithms.
Constraint Satisfaction Problems
Max 3 SAT
( x1  x2  x3 )(x2  x3  x5 )(x2  x3  x5 )(x5  x4  x1 )
Find an assignment that satisfies the
maximum number of clauses.
Variables                         {x1 ,x2 , x3 , x4 , x5}
Finite Domain                     {0,1}
Constraints                       Clauses
Kind of constraints permitted
Different CSPs
Gap for MaxCUT
Approximability of CSPs                                     Algorithm = 0.878
Hardness = 0.941
ALGORITHMS                           MAX k-CSP
[Charikar-Makarychev-Makarychev 06]        Unique Games
[Goemans-Williamson]                  MAX 3-CSP
[Charikar-Wirth]                                         NP HARD
MAX 3-AND
[Lewin-Livnat-Zwick]
[Charikar-Makarychev-Makarychev 07]              MAX 3-MAJ
[Hast]                        MAX E2 LIN3
[Charikar-Makarychev-Makarychev 07]               MAX 3 DI-CUT
[Frieze-Jerrum]
[Karloff-Zwick]                       MAX 4-SAT
[Zwick SODA 98]                         MAX DI CUT
[Zwick STOC 98]
MAX 3-SAT
[Zwick 99]
[Halperin-Zwick 01]                            MAX CUT
[Goemans-Williamson 01]                         MAX 2-SAT
[Goemans 01]                          MAX Horn SAT
[Feige-Goemans]
[Matuura-Matsui]                              MAX k-CUT
[Trevisan-Sudan-Sorkin-Williamson]
0                                         1
Given linear equations of       x-y = 11 (mod 17)
the form:                        x-z = 13 (mod 17)
Xi – k = cik mod p
TowardsXbridging this gap,              …
Satisfy maximum number of                ….
In 2002, Subhash Khot introduced the
equations.                      z-w = 15(mod 17)

Unique Games Conjecture [Khot 02] [KKMO]
Unique Games Conjecture
For every ε> 0, for large enough p,
Given : 1-ε (99%) satisfiable system,
NP-hard to satisfy
ε (1%) fraction of equations.
Unique Games Conjecture
A notorious open problem.
Algorithm                                     On (1-Є) satisfiable instances
[Khot 02]                                        1  O( p 2 1/ 5 log(1/  ) )
[Trevisan]                                          1  O(3  log n )
[Gupta-Talwar]                                      1 – O(ε logn)

[Charikar-Makarychev-Makarychev]                       p  /( 2 )
[Chlamtac-Makarychev-Makarychev]                1  O( log n log p )
[Arora-Khot-Kolla-Steurer-Tulsiani-Vishnoi]                1
1 log
    
Hardness Results:
No constant factor approximation for unique games. [Feige-
Reichman]
UGC HARD         NP HARD
Assuming UGC
MAX k-CSP                                UGC Hardness
Unique Games                               Results
MAX 3-CSP                      [Khot-Kindler-Mossel-O’donnell]
MAX 3-AND                                [Austrin 06]
For MaxCut, Max-2-SAT,                       [Austrin 07]
MAX 3-MAJ
Unique Games   based hardness
[Khot-Odonnell]
MAX E2 LIN3                          [Odonnell-Wu]
MAX 3 DI-CUT        =          [Samorodnitsky-Trevisan]
approximation obtained by Semidefinite programming!
MAX 4-SAT
MAX DI CUT
MAX 3-SAT
MAX CUT
MAX 2-SAT
MAX Horn SAT
MAX k-CUT

0                                  1
The Connection

MAX k-CSP
Unique Games         UGC Hard
How General a CSP?   MAX 3-CSP                 How Simple an SDP?
Theorem:                                        [Raghavendra 08]
MAX 3-AND
every CSP,
Assuming Unique Games Conjecture, For takes near linear
MAX 3-MAJ
Can Specify
Theorem:
“the simplest semidefinite programs give the[Raghavendra08]
10% of 3-Clauses      MAX E2 LIN3              best
time in the size of
approximation computableoptimal for every CSP under
generic algorithm that 3 DI-CUT
A 70% of Cut constraints       is efficiently.”
MAX                   the CSP.
GENERIC as all known algorithms)
UGC! 2-SAT constraints (at least as good
20% of
MAX 4-SAT          (techniques from
ALGORITHM
MAX DI CUT          [Arora-Kale])
MAX 3-SAT
MAX CUT
MAX 2-SAT
MAX Horn SAT
MAX k-CUT
3-way Cut

B
3-Way Cut:
10                            “Separate the 3-terminals
15
A        1
7                  while separating the
1                   minimum number of edges”
3
C                               A generalization of the
classic s-t cut problem

B
[Karger-Klein-Stein-Thorup-Young]
A 12/11 factor approximation algorithm
for 3-Way Cut

A                             C
Graph Labelling Problems

ALGORITHMS
Generalizations of 3-Way Cut              [Calinescu-Karloff-Rabani 98]
[Chekuri-Khanna-Naor-Zosin]
• k-Way Cut
[Calinescu-Karloff-Rabani 01]
• 0-Extension                                     [Gupta-Tardos]
• Class of Metric Labelling Problems   [Karger-Klein-Stein-Thorup-Young]
[Kazarnov 98]
[Kazarnov 99]
[Kleinberg-Tardos]

Theorem:       [Manokaran-Naor-Raghavendra-Schwartz]
Assuming Unique Games Conjecture,
The “earthmover linear program” gives the best
approximation for every graph labelling problem.
Ranking Teams?
Maximum Acyclic Subgraph
Rank teams so that result of
“Given a directed of
maximum numbergraph,
order the vertices to
games agrees with the
maximize the number of
ranking
forward edges.”

•Best known approximation
algorithm :
“Output a Random Ordering!”
Result

Theorem:               [Guruswami-Manokaran-Raghavendra]
Assuming Unique Games Conjecture,
The best algorithm’s output is as good as a random ordering.

More generally,

Theorem:               [Guruswami-Manokaran-Raghavendra]
Assuming Unique Games Conjecture, For every Ordering CSP,
a simple SDP relaxation gives the best approximation.
The UG Barrier

If UGC is true,
Constraint Satisfaction
Problems
Then Simplest SDPs
give the best
approximation
Graph Labelling Problems        UGC    possible.
HARD
If UGC is false,
Ordering CSPs

Hopefully, a new
Kernel Clustering Problems
algorithmic
technique will arise.
Grothendieck Problem
Even if UGC is false

Generic Approximation                SDP Lower Bounds
Algorithm for CSPs            For problems like Maximum Acyclic
Subgraph, Multiway Cut,
At least as good as all known
algorithms for CSPs.

Computing Approximation Ratios
An algorithm to compute the value of
approximation ratio obtained by a certain SDP
An Interesting Aside

Grothendieck’s Inequality (1953)
There exists constant KG such that, for all matrices (aij)

1.67 < KG < 1.78           [Krivine]
In computer science terminology,

Grothendieck constant = Approximation given by the
Semidefinite relaxation for the Bipartite

Algorithm to compute Grothendieck constant [Raghavendra-Steurer09]
SEMIDEFINITE PROGRAMMING
Max Cut
Max CUT
Input :
10
A weighted graph G
15                    7
1
1           Find :
3                        A Cut with maximum
number/weight of
crossing edges
Fraction of
crossing edges
Max Cut SDP

-1 Semidefinite Program
-1
1       10

15       1
-1
7
x v v
Variables : v1 , x2 … xn
1
1                                   1                  xi = i1 2 = -1
| v | or 1
-1                -1
3       -1

1
-1
Maximize          E | v v
4 ( i , j )
wij ( xii  x j )|2

Relax all the xi to be unit vectors instead of {1,-1}.
MaxCut Rounding
v2

Cut the sphere by a random
v1             v3
hyperplane, and output the
induced graph cut.

-A 0.878 approximation for
the problem.
v5
[Goemans-Williamson]

v4
The Simplest
Max Cut SDP:                         Relaxation for
MaxCut        v2
Embedd the graph on the
N - dimensional unit ball,                    v1                  v3

Maximizing

¼ (Average Squared Length
v5
of the edges)
v4
In the integral solution, all the vi are 1,-1. Thus they satisfy
For example :           (vi – vj)2 + (vj – vk)2 ≥ (vi – vk)2

Assuming UGC, No additional constraint helps!
Building on the work of [Khot-Vishnoi],
[Raghavendra-Steurer 09]
Adding all valid constraints on at most
Possibility:
2^O((loglogn)1/4 ) variables to the simple SDP does not
disprove the Unique Games Conjecture
Adding a simple constraint on every 5 variables
yields a better approximation for MaxCut,

Constraint the UG barrier and disproves
Breaches Satisfaction
Unique Games Conjecture! Metric Labelling Problems
Problems
Ordering Constraint Satisfaction
Problems                    Kernel Clustering Problems

Grothendieck Problem
So far :
• Unique Games Barrier
• Semidefinite Programming
technique (Maxcut example)

Coming Up :
• Generic Algorithm for CSPs
• Hardness Result for MaxCut.
Generic Algorithm for CSPs
Semidefinite Program for CSPs
( x1  x2  x3 )(x2  x3  x5 )(x2  x3  x5 )(x5  x4  x1 )
Variables :                                   Constraints :
For each variable Xa                          For each clause P,
0 ≤μ(P,α) ≤ 1
Vectors {V(a,0) , V(a,1)}                    Xa = 1                      V(a,0) = 0


( P , )  1 (a,1)
V      = 1

For each clause P = (xa ν xb ν xc),                 Xa = 0                            V(a,0) = 1
V(a,1) = 0
Scalar variables                  For each clause P (xa ν xb ν xc),
μ(P,000) , μ(P,001) , μ(P,010) , μ(P,100) ,                  each 1, X X 1
If XFor 0, Xb =pairc = a , Xb in P,
a =
μ(P,011) , μ(P,110) , μ(P,101) , μ(P,111)     consitency between vector and LP
variables. = 0
μ(P,000)             μ(P,011) = 1
μ(P,001) = 0                μ(P,110) = 0
Objective Function :                                μ(P,010) = 0
V(a,0) ∙V                  μ(P,101) = 0
= μ(P,000) + μ(P,001)
μ(P,100) = 0 (b,0)
  P( )( P, )
μ(P,111) = 0
V(a,0) ∙V (b,1) = μ(P,010) + μ(P,011)
Clauses assignments                                V(a,1) ∙V (b,0) = μ(P,100) + μ(P,101)
P        {0,1}3                                  V(a,1) ∙V (b,1) = μ(P,100) + μ(P,101)
Semidefinite Relaxation for CSP
SDP solution for =:                    Example of local distr.:
Á = 3XOR(x3, x4, x7)
for every constraint Á in =
x3x4   x7   ¹Á
- local distributions ¹Á over                0 0    0    0.1
0 0    1    0.01
assignments to the variables of Á          0 1    0    0
…
for every variable xi in =                   1 1    1    0.6

- vectors vi,1 , … , vi,q
constraints                           Explanation of constraints:
first and second moments of
distributions are consistent
(also for first moments)           and form PSD matrix

SDP objective:
maximize
v2
Rounding Scheme
[Raghavendra-Steurer]        v1                         v3

STEP 1 : Dimension Reduction

• Project the SDP solution along
say 100 random directions.                                           v5

Map vector V
V → V’ = (V∙G1 , V∙G2 , … V∙G100)                      v4

STEP 2 : Discretization
•Pick an Є –net for the
100 dimensional sphere
• Move every vertex to the nearest
point in the Є –net                           Constant dimensions
STEP 3 : Brute Force
FINITE MODEL
•Find a solution to the new
Graph on Є –net points
instance by brute force.
HARDNESS RESULT FOR MAXCUT
The Goal

Theorem:                                   [Raghavendra 08]
Assuming Unique Games Conjecture, For MaxCut,
“the simple semidefinite program give the best
approximation computable efficiently.”

UG Hardness
HARD INSTANCE G            Assuming UGC,
Suppose for an instance G,     On instances with MaxCut = C,

the SDP value = C    It is NP-hard to find a MaxCut
The actual MaxCut value = S    better than S
v2
Dimension Reduction                      v1                     v3
Max Cut SDP:

Embed the graph on the
N
100- dimensional unit ball,
v5
Maximizing
v4
¼ (Average Squared Length
of the edges)

Project to random 1/ Є2
Constant dimensional hyperplane
dimensional space.
New SDP Value = Old SDP Value + or - Є
Making the Instance Harder
v2
SDP Value = Average Squared
v1                            v3
Length of an Edge

Transformations
• Rotation does not change the
v5     SDP value.
• Union of two rotations has the
v4                       same SDP value
Sphere Graph H :
Union of all possible rotations of G.

SDP Value (Graph G) = SDP Value ( Sphere Graph H)
Making the Instance Harder
v2

v1                     v3       MaxCut (H) = S

MaxCut (G) ≥ S
v5
Pick a random rotation of G and
read the cut induced on it.
v4             Thus,
v2
v1              v3
MaxCut (H) ≤ MaxCut(G)
v5
v4
SDP Value (G) = SDP Value (H)
v2

Hypercube Graph                                   v1             v3

For each edge e, connect
every pair of vertices in                                         v5
hypercube separated by
SDP Solution     v4
the length of e

Generate Edges of Expected Squared
Length = d

1) Starting with a random x Є {-1,1}100 ,
1) Generate y by flipping each bit of x
with probability d/4

Output (x,y)
100 dimensional hypercube : {-1,1}100
Dichotomy of Cuts
1             1

1
1                       A cut gives a function F on the
hypercube
F : {-1,1}100 -> {-1,1}
-1

-1
Dictator Cuts
-1
F(x) = xi

Hypercube = {-1,1}100   Cuts Far From Dictators
(influence of each coordinate
on function F is small)
v2
v1             v                       X            Dictator Cuts
v5                               For each edge e = (u,v),
u                                     connect every pair of vertices
Y
in hypercube separated by
100 dimensional hypercube   the length of e

Pick an edge e = (u,v), consider all edges in hypercube
corresponding to e
Number of
Fraction of red            Fraction of                 bits in which
edges cut by        = dictators that =                 X,Y differ
horizontal                 cut one such                       =
dictator .                 edge (X,Y)                     |u-v|2/4
Fraction of edges cut by dictator = ¼ Average Squared
Distance
Value of Dictator Cuts = SDP Value (G)
v2                            -1                   Cuts far from
v1               v3                        -1
-1
Dictators
v5
1
v4
1            1
100 dimensional hypercube

v2                 Intuition:
v1                 v3
Sphere graph          : Uniform on all directions

v5   Hypercube graph : Axis are special directions
v4
If a cut does not respect the axis, then it should
not distinguish between Sphere and Hypercube
graphs.
The Invariance Principle
Central Limit Theorem

``Sum of large number of {-1,1} random variables
has similar distribution as
Sum of large number of Gaussian random variables.”

Invariance Principle for Low Degree Polynomials
[Rotar] [Mossel-O’Donnell-Oleszkiewich], [Mossel 2008]

“If a low degree polynomial F has no influential
coordinate, then F({-1,1}n) and F(Gaussian) have
similar distribution.”
Hypercube vs Sphere

H
P : sphere -> Nearly {-1,1}
F:{-1,1}100-> {-1,1}           is the multilinear extension
is a cut far from every       of F
dictator.
By Invariance Principle,
MaxCut value of F on hypercube   ≈   Maxcut value of P on
Sphere graph H
v2
v1             v3   Hyper Cube Graph
[Dictatorship Test]
v5       [Bellare-Goldreich-Sudan]
v4
Graph G
Completeness
Value of Dictator Cuts
= SDP Value (G)

Soundness
Cuts far from dictators
≤ MaxCut( Sphere Graph)
Hypercube = {-1,1}100              ≤ MaxCut( G)
UG Hardness
UG Hardness
Dictatorship                 “On instances, with
Test                   value C, it is NP-hard to
Completeness C
Soundness S     [KKMO]     output a solution of
value S, assuming UGC”

In our case,

Completeness = SDP Value (G)
Soundness    = MaxCut(G)
Cant get better approximation than SDP,
assuming UGC!
FUTURE WORK
Understanding Unique Games

“Unique Games Conjecture is false→New algorithms?”

[Reverse Reduction from MaxCut/CSPs to Unique Games]

“Stronger SDP relaxations → Better approximations?”
equivalently,
“Can stronger SDP relaxations disprove the UGC?”

Unique Games and Expansion of small sets in graphs?
Beyond
Beyond CSPs                Approximability

Dichotomy Conjecture
Semidefinite Programming   “Every CSP is polynomial
or UG hardness results     time solvable or NP-hard”
for problems beyond CSP

Example :                  [Kun-Szegedy] Techniques from
approximation could be useful here.
1) Metric Travelling
Salesman Problem,       1) When do local
2) Minimum Steiner Tree.      propogation algorithms
work?
2) When do SDPs work?
Thank You
Given a function
Dictatorship Test              F : {-1,1}R     {-1,1}
•Toss random coins
•Make a few queries to F
•Output either ACCEPT or
REJECT

F is a dictator function      F is far from every
F(x1 ,… xR) = xi          dictator function
(No influential coordinate)

Pr[ACCEPT ] =                  Pr[ACCEPT ] =
Completeness                    Soundness
A Dictatorship Test for Maxcut
A dictatorship test is a graph
G on the hypercube.
A cut gives a function F on the
hypercube

Completeness
Value of Dictator Cuts
F(x) = xi
Soundness
Hypercube = {-1,1}100   The maximum value
attained by a cut far from
a dictator
Connections
SDP Gap
Instance
SDP = 0.9
OPT = 0.7
[Khot-Vishnoi]
[This Work]                                               For sparsest cut, max cut.

Dictatorship                                             UG
Test                                               Hardness
Completeness = 0.9                                       0.9 vs 0.7
Soundness = 0.7
[Khot-Kindler-Mossel-O’Donnell]

All these conversions hold for very general
classes of problems
In Integral Solution
General Boolean 2-CSPs   vi = 1 or -1
V0 = 1

Total PayOff

Triangle Inequality
2-CSP over {0,..q-1}

Total PayOff
Arbitrary k-ary GCSP

•SDP is similar to the one used by [Karloff-Zwick]
Max-3-SAT algorithm.
•It is weaker than k-rounds of Lasserre / LS+
heirarchies
1) Tests of the verifier are same as
Key Lemma                  the constraints in instance G
2) Completeness = SDP(G)

DICTG
Any                                  Dictatorship Test
CSP Instance                                on functions
G                                   F : {-1,1}n ->{-1,1}

Any                                   RoundF
Function                             Rounding Scheme
F: {-1,1}n → {-1,1}                       on CSP Instances G

If F is far from a dictator,
RoundF (G)     ≈ DICTG (F)
Key Lemma : Through An Example
SDP:
1
Variables : v1 , v2 ,v3
|v1|2 = |v2|2 = |v3|2 =1
2        3
Maximize
1
3

| v1  v2 |2  | v2  v3 |2  | v3  v1 |2   
c = SDP Value
Local Random Variables                 v1 , v2 , v3 = SDP Vectors

Fix an edge e = (1,2).
1

A12         A13       There exists random
variables a1 a2 taking
A23         3
2
values {-1,1} such that:
there is a = v v
For every edge, E[a a ] local ∙distribution over
1 2        1 2
integral solutions such that:
All the moments of order at most 2 match the
E[a12] = |v1|2        E[a22] = |v2|2
inner products.
A12,A23,A31 = Local Distributions
Analysis
Pick an edge (i,j)                                   Max Cut Instance

Generate ai,aj in {-1,1}R as follows:                                         1

The kth coordinates aik,ajk come
from distribution Aij
3
Accept if
Input Function:
F(ai) ≠ F(aj)
F : {-1,1}R -> {-1,1}

1 1                                  1                                 1                            2 
 4 E A12 [( F (a1 )  F (a2 )) ]  4 E A23 [( F (a2 )  F (a3 )) ]  4 E A31 [( F (a3 )  F (a1 )) ]
2                                 2

3                                                                                                     
A12,A23,A31 = Local Distributions
Completeness
Input Function is a Dictator :                 F(x) = x1

1 1 1
1                           2 2 1 1                         22 1    1                        2 2 
 4 E A ( [( 11 (a2 21 ] 4 4 23 A F ( a ) (a  4 31 [( a31 ) a F ( 1  
 4 E A12 [( F12a1 )a F  a)) )]   E AE[(23 [(a221Fa313))) ] ]  4 E A31 [( F (a3  11 ) a] ]
3 3
))

Suppose (a1 ,a2) is sampled from A12 then :
E[a11 a21] = v1∙ v2           E[a112] = |v1|2                       E[a212] = |v2|2

EA12 [(a1  a2 ) ] | v1  v2 |
2                 2

Summing up, Pr[Accept] = SDP Value(v1 , v2 ,v3)
c = SDP Value
Global Random Variables               v1 , v2 , v3 = SDP Vectors

g = random Gaussian vector.
1
(each coordinate generated by
i.i.d normal variable)
B                      b1 = v1 ∙ g
3
2                            b2 = v2 ∙ g
b3 = v3 ∙ g
There is a v ∙ v E[b b ] = ∙ v E[b,b3) = v ∙ v
E[b1 b2] = global distributionvB=(b1 ,b2 3 b1]over 3real
1 2      2 3      2 3                     1
numbers such that:
All the moments of order at most 2 match the
E[b12] = |v1|2 E[b22] = |v2|2 E[b32] = |v3|2
inner products.
1
Rounding with Polynomials
Input Polynomial : F(x1 ,x2 ,.. xR)
B         3
2
Generate
b1 = (b11 ,b12 ,… b1R)
b2 = (b21 ,b22 ,… b2R)
b3 = (b31 ,b32 ,… b3R)
with each coordinate (b1t ,b2t ,b3t) according to global
distribution B

Compute F(b1),F(b2) ,F(b3)
Round      F(b1),F(b2),F(b3) to {-1,1}
Output the rounded solution.

1 1                               1                              1                         2 
 4 EB [( F (b1 )  F (b2 )) ]  4 EB [( F (b2 )  F (b3 )) ]  4 EB [( F (b3 )  F (b1 )) ]
2                              2

3                                                                                            
Invariance
Suppose F is far from every dictator then since A12
and B have same first two moments,
F(a1),F(a2) has nearly same distribution as
F(b1),F(b2)
1                               1
•      E A12 [(F (a1 )  F (a2 )) ]  EB [(F (b1 )  F (b2 ))2 ]
2

4                               4

•    F(b1), F(b2) are close to {-1,1}
Rounding Scheme
(For Boolean CSPs)

Rounding Scheme was discovered by the
reversing the soundness analysis.
This fact was independently observed by Yi Wu
SDP Rounding Schemes
SDP Vectors                       For any CSP, it is enough to
(v1 , v2 .. vn )                  do the following:
Random Projection         Instead of one random
projection, pick sufficiently
Projections                      many projections
(y1 , y2 .. yn )

Process the projections
Use a multilinear
Assignment
polynomial P to process the
projections
Rounding By Polynomial P(y1,… yR)
Roughly             Formally
Sample R Random Sample R independent vectors : w(1), w(2) ,.. w(R)
Directions      Each with i.i.d Gaussian components.
Project each vi along all directions w(1), w(2) ,..
Project along   w(R)
them            Yi(j) = v0∙vi + (1-ε)(vi – (v0∙vi)v0) ∙ w(j)
Compute P on        Compute
projections                   xi = P(Yi(1) , Yi(2) ,.. Yi(R))
Round the output If xi > 1,         xi = 1
of P             If xi < -1,        xi = -1
If xi is in [-1,1]
xi = 1 with probability (1+xi)/2
-1 with probability (1-xi)/2
R is a constant parameter
Algorithm

Solve SDP(III) to obtain vectors (v1 ,v2 ,… vn )
Smoothen the SDP solution (v1 ,v2 ,… vn )

For all multlinear polynomials P(y1 ,y2, .. yR) do
Round using P(y1 ,y2, .. yR)
Output the best solution obtained
Discretization
“For all multilinear polynomials P(y1 ,y2, .. yR)
do”

- All multilinear polynomials with coefficients
bounded within [-1,1]
- Discretize the set of all such multi-linear
polynomials
There are at most a constant number of such
polynomials.
Smoothening SDP Vectors
Let u1 ,u2 .. un denote the SDP vectors
corresponding to the following distribution
over integral solutions:
``Assign each variable uniformly and
independently at random”

Substitute
vi* ∙ vj* = (1-ε) (vi ∙ vj) + ε (ui∙ uj)
Semidefinite                          Linear program over the
Program                             inner products of vectors

Simplest SDP for MaxCut                     In the integral solution,
all the vi are 1,-1
Variables : v1 , v2 … vn
| v i |2 = 1                      Thus they satisfy
1
Maximize 4 (iE , j )
wij (vi  v j ) 2
In the integral solution,
all the i vj) + (v
Example Constraint: (viv–are2 1,-1 j – vk)2 ≥ (vi – vk)2

Thus they satisfy
Thank You
MAX k-CSP
Unique Games
MAX 3-CSP
MAX 3-AND
MAX 3-MAJ
MAX E2 LIN3
GENERIC
MAX 3 DI-CUT
MAX 4-SAT
ALGORITHM
MAX DI CUT
MAX 3-SAT
MAX CUT
MAX 2-SAT
MAX Horn SAT
MAX k-CUT

0                              1
MAX k-CSP
Unique Games
MAX 3-CSP
MAX 3-AND
MAX 3-MAJ
MAX E2 LIN3
MAX 3 DI-CUT
MAX 4-SAT
MAX DI CUT
MAX 3-SAT
MAX CUT
MAX 2-SAT
MAX Horn SAT
MAX k-CUT

0                              1
MAX k-CSP
Unique Games
MAX 3-CSP
MAX 3-AND
MAX 3-MAJ
MAX E2 LIN3
MAX 3 DI-CUT
MAX 4-SAT
MAX DI CUT
MAX 3-SAT
MAX CUT
MAX 2-SAT
MAX Horn SAT
MAX k-CUT

0                              1
Approximability of CSPs
ALGORITHMS                          Unique Games
[Charikar-Makarychev-Makarychev 06]           MAX 3-CSP
[Goemans-Williamson]                          MAX CUT
[Charikar-Wirth]
[Lewin-Livnat-Zwick]                         MAX 2-SAT
[Charikar-Makarychev-Makarychev 07]       MAX k-CSP
[Hast]                          MAX 3-SAT
[Charikar-Makarychev-Makarychev 07]              MAX DI CUT
[Frieze-Jerrum]
MAX 4-SAT
[Karloff-Zwick]
[Zwick SODA 98]                              MAX k-CUT
[Zwick STOC 98]                          MAX Horn SAT
[Zwick 99]
[Halperin-Zwick 01]                       MAX 3 DI-CUT
[Goemans-Williamson 01]             MAX E2 LIN3
[Goemans 01]                   MAX 3-AND
[Feige-Goemans]                    MAX 3-MAJ
[Matuura-Matsui]
0                              1

```
Related docs
Other docs by pptfiles