M u lti-la b e l P o tts Complexity of Discrete Energy ...mengtial/pdf/Li, Mengtian - ECCV 2016 -...

Complexity of Discrete Energy Minimization Problems

Alexander Shekhovtsov2

Energy minimization is NP-hard ☹

Is it approximable? Not yet resolved

Sometimes yes: Potts, Metric, Logic MRF ☺

We prove that QPBO, planar energy

with 3+ labels, and general energy mini-

mization are all inapproximable

Useful for algorithm design — finding

“good” subclasses

In practice, useful for model selection

Abstract

Mengtian Li1 Daniel Huber1

Carnegie Mellon University1 Graz University of Technology2

Graph with label space

Pairwise energy

Quadratic Pseudo-Boolean Optimization

(QPBO)

General energy minimization

Energy Minimization Formulation

Optimization problems

Approximation ratio

APX — constant ratio approximation

F-APX — approximation ratio is a function

of class F of the input bit length

Relations of complexity classes

Optimization & Approximation

Complexity Axis & Main Results

Complexity ClassLabel Space,

Interaction Type,

Graph Structure

NP-hard

NPO

APX

(Bounded

Approximation)

exp-APX

(Unbounded

Approximation)

PO

(Global

Optimum)

Multi-label Potts

Logic MRF

Convex Interaction

Binary Outerplanar

Submodular

3-label Planar

Binary or Multi-label

Bounded Treewidth

Non-deterministic Polynomial time Optimization (NPO)

The set of instances is recognizable in polynomial time

The solution’s feasibility is verifiable in polynomial time

A positive objective value

Polynomial time Optimization (PO)

The problem is in NPO, and it is solvable in polynomial

time

Approximation-Preserving reduction (AP-reduction)

Reduce NPO problem P1 to another NPO problem P2

For a given positive constant α, the mappings must satis-

fy,

C-hard & C-complete

A problem is C-hard if any problem in complex-

ity class C can be reduced to it

A C-hard problem is C-complete if it belongs to C

Intuitively, a complexity class C specifies the upper

bound on the hardness of the problems within, C-

hard specifies the lower bound, and C-complete ex-

actly specifies the hardness

Problem W3SAT-triv

INSTANCE: Boolean CNF formula F with variables x1, …,

xn and each clause assuming exactly 3 variables; non-

negative integer weights w1, …, wn

SOLUTION: Truth assignment τ to the variables that either

satisfies F or assigns the trivial, all-true assignment

MEASURE:

Details

Proof Scheme

Energy minimization problems vary greatly

in approximation ratio

Where do QPBO and general energy minimi-

zation fall on this axis?

Theorem:

QPBO (binary labels) is complete in exp-APX.

Theorem:

Planar energy with 3+ labels is complete in exp-APX.

Theorem:

General energy minimization is complete in exp-APX.

Bounded approximation ratio ☺

Indicates a class of practical interest

Useful for algorithm design

Do not try to prove approximation guar-

antee if

Model includes QPBO, planar 3-label,

or general energy minimization

Or you can build AP-reduction from them

2 2 1 2

* *

2 2 1 1

( ) ( ( ))implies 1 ( 1)

( ) ( )

f y f yr r

f y f y

References

[1] G. Ausiello et al., Complexity and approximation: Combinatorial optimi-

zation problems and their approximability properties. Springer (1999)

[2] P. Orponen et al., On approximation preserving reductions: complete

problems and robust measures. Technical Report (1987)

[3] H. Ishikawa, Transformation of general binary MRF minimization to the

first-order case. PAMI 33(6), 1234–1249 (2011)

NP Machine

Weighted 3-SAT

QPBO

Planar energy with 3+ labels

General energy with 3+ labels

AP-reduction

AP-reduction

AP-reduction

Planar reduction

AP-reduction [2, 1]

4 X

4 X

1min ( )

n

i iiw x

x1 : instance of P1

solve P2

y1 : solution of P1

x2 : instance of P2

y2 : solution of P2