Jayant chm

October 3, 2012 Jayant Apte. ASPITRG 1

Polyhedral Computation

Jayant Apte ASPITRG


Part I


The Three Problems ofPolyhedral Computation

● Representation Conversion● H-representation of polyhedron

● V-Representation of polyhedron

● Projection●

● Redundancy Removal● Compute the minimal representation of a polyhedron


Outline● Preliminaries

● Representations of convex polyhedra, polyhedral cones● Homogenization - Converting a general polyhedron in to

a cone in

● Polar of a convex cone

● The three problems● Representation Conversion

– Review of LRS

– Double Description Method

● Projection of polyhedral sets

– Fourier-Motzkin Elimination

– Block Elimination

– Convex Hull Method (CHM)

● Redundancy removal

– Redundancy removal using linear programming


Outline for Part 1● Preliminaries

● Representations of convex polyhedra, polyhedral cones

● Homogenization – Converting a general polyhedron in to a cone in

● Polar of a convex cone ● The first problem

● Representation Conversion– Review of LRS– Double Description Method


Preliminaries


Convex Polyhedron


Examples of polyhedra

Bounded- Polytope Unbounded - polyhedron


H-Representation of a Polyhedron


V-Representation of a Polyhedron


Example

(1,0,0)

(0,0,0)

(0,1,0)

(1,1,0)

(0,1,1)

(0.5,0.5,1.5)(1,1,1)

(0,0,1)


Switching between the two representations : Representation Conversion Problem

● H-representation V-representation: The Vertex Enumeration problem

● Methods:

● Reverse Search, Lexicographic Reverse Search● Double-description method

● V-representation H-representation

The Facet Enumeration Problem

● Facet enumeration can be accomplished by using polarity and doing Vertex Enumeration


Polyhedral Cone


A cone in


Homogenization


H-polyhedra


Example(d=2,d+1=3)


Example


V-polyhedra


Polar of a convex cone


Polar of a convex cone

Looks like an H-representation!!!

Our ticket back to the original cone!!!


Polar: Intuition


Polar: Intuition


Polar: Intuition


Polar: Intuition


Use of Polar

● Representation Conversion● No need to have two different algorithms i.e. for

and .

● Redundancy removal● No need to have two different algorithms for

removing redundancies from H-representation

and V-representation

● Safely assume that input is always an H-representation for both these problems


Polarity: Intuition


Polar: Intuition

Perform Vertex Enumeration on this cone.

Then take polar again to get facets of this cone


Problem #1Representation Conversion


Review of Lexicographic Reverse Search


Reverse Search: High Level Idea

● Start with dictionary corresponding to the optimal vertex

● Ask yourself 'What pivot would have landed me at this dictionary if i was running simplex?'

● Go to that dictionary by applying reverse pivot to current dictionary

● Ask the same question again

● Generate the so-called 'reverse search tree'


Reverse Search on a Cube

Ref.David Avis, lrs: A Revised Implementation of the Reverse Search Vertex Enumeration Algorithm


Double Description Method

● First introduced in:

“ Motzkin, T. S.; Raiffa, H.; Thompson, G. L.; Thrall, R. M. (1953). "The double description method". Contributions to the theory of games. Annals of Mathematics Studies. Princeton, N. J.: Princeton University Press. pp. 51–73”

● The primitive algorithm in this paper is very inefficient.

(How? We will see later)

● Several authors came up with their own efficient implementation(viz. Fukuda, Padberg)

● Fukuda's implementation is called cdd


Some Terminology


Double Description Method:The High Level Idea

● An Incremental Algorithm

● Starts with certain subset of rows of H-representation of a cone to form initial H-representation

● Adds rest of the inequalities one by one constructing the corresponding V-representation every iteration

● Thus, constructing the V-representation incrementally.


How it works?


How it works?


Initialization


Initialization

Very trivial and inefficient


Example: Initialization


Example: Initialization


Example : Initialization

4.0000 14.0000 9.0000 4.0000 4.0000 9.0000 1.0000 1.0000 1.0000

-1 -1 18 1 -1 0 0 1 -4

A B C


How it works?


Iteration: Insert a new constraint


Iteration 1 4.0000 14.0000 9.0000 4.0000 4.0000 9.0000 1.0000 1.0000 1.0000

A B C


Iteration 1: Insert a new constraintConstraint 2


Iteration 1: Insert a new constraint




Main Lemma for DD Method




Iteration 1: Get the new DD pair

10.0000 12.0000 4.0000 9.0000 4.0000 6.0000 4.0000 9.0000 1.0000 1.0000 1.0000 1.0000

-1 -1 18 1 -1 0 0 1 -4 -1 1 6


Iteration 2


Iteration 2: Insert new constraint






Get the new DD pair

-1 -1 18 1 -1 0 0 1 -4 -1 1 6 0 -1 8

9.2000 10.0000 8.0000 10.0000 12.0000 4.0000 8.0000 8.0000 8.0000 4.0000 6.0000 4.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000


Iteration 3


Get the new DD pair

-1 -1 18 1 -1 0 0 1 -4 -1 1 6 0 -1 8 1 1 -12

6.2609 6.4000 6.0000 8.0000 7.2000 9.2000 10.0000 8.0000 10.0000 12.0000 5.7391 5.6000 6.0000 4.0000 4.8000 8.0000 8.0000 8.0000 4.0000 6.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

A B C D E F G H I J


Termination


Efficiency Issues

● Is the implementation I just described good enough?


Efficiency Issues

● Is the implementation I just described good enough?

● Hell no!– The implementation just described suffers from

profusion of redundancy


Efficiency IssuesWe can totallydo without this one!!


Efficiency IssuesAnd without all these!!!


Efficiency Issues


The Primitive DD method


What to do?

● Add some more structure● Strengthen the Main Lemma


Add some more structure


Some definitions




Algebraic Characterization of adjacency


Combinatorial Characterization of adjacency (Combinatorial Oracle)


Example: Combinatorial Adjacency Oracle


Example: Combinatorial Adjacency Oracle

Only the pairs and will be used to generate new rays


Strengthened Main Lemma for DD Method


Procedural Description


Part 2


● Projection of polyhedral sets– Fourier-Motzkin Elimination– Block Elimination– Convex Hull Method (CHM)

● Redundancy removal– Redundancy removal using linear programming


Projection of a Polyhedra


Fourier-Motzkin Elimination

● Named after Joseph Fourier and Theodore Motzkin


How it works?


How it works? Contd...


Efficiency of FM algorithm


● First appears in “C. Lassez and J.-L. Lassez, Quantifier elimination for conjunctions of linear constraints via aconvex hull algorithm, IBM Research Report, T.J. Watson Research Center (1991)”

● Found to be better than most other existing algorithms when dimension of projection is small

● Cited by Weidong Xu, Jia Wang, Jun Sun in their ISIT 2008 paper “A Projection Method for Derivation of Non-Shannon-Type Information Inequalities”

Convex Hull Method(CHM)


CHM intuition


CHM intuition


CHM intuition (12,6,6)

(12,6)


CHM intuition


Moral of the story

● We can make decisions about without actually having its H-representation.

● It suffices to have , the original polyhedron● We run linear programs on to make these

decisions


Input:Faces A of P

Construct an initial Approximation of extreme points of

Perform Facet Enumeration on

to get

Are all inequalities In faces

Of ?

Stop

Take an inequality from that is not a face of

and move it outward To find a new extreme point r

Update by adding r to it

Yes

No

Convex Hull Method:Flowchart


Example


Example


Example



Input:Faces A of P


Perform Facet Enumeration on

to get

Are all inequalities In facets

Of ?

Stop





How to get the extreme points of initial approximation?


Example: Get the first two points






Get rest of the points so you have a total of d+1 points


Get the rest of the points so you have a total of d+1 points


Get the rest of the points so you have a total d+1 points


How to get the initial facets of the projection?


How to get the initial facets of the projection?


Input:Faces A of P


Incrementally updateThe Convex Hull (Perform Facet

Enumeration on to get )


Of ?

Stop




Yes

No



Input:Faces A of P





Of ?

Stop




Yes

No



Input:Faces A of P





Of ?

Stop

Take an inequality from that is not a facet of



Yes

No



Incremental refinement

● Find a facet in current approximation of that is not actually the facet of

● How to do that?



This inequality isNot a facet of




Input:Faces A of P





Of ?

Stop

Take an inequality from that is not a facet of



Yes

No



How to update the H-representationto accommodate new point ?


Example


Example


Example: New candidate facets



Determine validity of The candidate facet And also its sign


Example


Example




Example


Example


Example


2nd iteration

Not a facet of


3rd iteration

Not a facet of


4th iteration: Done!!!


Comparison of Projection Algorithms

● Fourier Motzkin Elimination and Block Elimination doesn't work well when used on big problems

● CHM works very well when the dimension of projection is relatively small as compared to the dimention of original polyhedron.

● Weidong Xu, Jia Wang, Jun Sun have already used CHM to get non-Shannon inequalities.


Computation of minimal representation/Redundancy

Removal


Definitions


References

●K. Fukuda and A. Prodon. Double description method revisited. Technical report, Department of Mathematics, Swiss Federal Institute of Technology, Lausanne, Switzerland, 1995

●G. Ziegler, Lectures on Polytopes, Springer, 1994, revised 1998. Graduate Texts in Mathematics.

●Tien Huynh, Catherine Lassez, and Jean-Louis Lassez. Practical issues on the projection of polyhedral sets. Annals of mathematics and artificial intelligence, November 1992

●Catherine Lassez and Jean-Louis Lassez. Quantifier elimination for conjunctions of linear constraints via a convex hull algorithm. In Bruce Donald, Deepak Kapur, and Joseph Mundy, editors, Symbolic and Numerical Computation for Artificial Intelligence. Academic Press, 1992.

●R. T. Rockafellar. Convex Analysis (Princeton Mathematical Series). Princeton Univ Pr.

●W. Xu, J. Wang, J. Sun. A projection method for derivation of non-Shannon-type information inequalities. In IEEE International Symposiumon Information Theory (ISIT), pp. 2116 – 2120, 2008.


Minkowski's Theorem for Polyhedral Cones

Weyl's Theorem for Polyhedral Cones