OperationsWithPolyhedra

For further help on the methods of the Polyhedron class, type help at the Matlab prompt. For instance, to obtain the help description for Minkowski summation in plus method, type

help Polyhedron/plus

or display the Matlab documentation that contains additional examples on the geometric methods

mptdoc

and search for the specific method there.

• plus - Minkowski summation
• minus - Pontryagin difference

• affineHull - affine hull
• affineMap - affine maps
• invAffineMap - inverse affine maps
• grid - regular grid of the polytope
• meshGrid - grid given as coordinates for 2D polytopes
• mldivide - set difference
• normalize - normalization of H-representation
• project - project a point onto a set
• projection - orthogonal projection onto axis
• slice - cuts of polyhedra
• triangulate - triangulation
• volume - computing a volume

Computation of the Chebyshev center corresponds to inscribing the largest ball inside a polyhedron which is implemented in the chebyCenter method. The ball is described as {$ \{ x ~|~ \| x-x_c \|_2 \le r \} $} where {$x_c$} is the center of the ball and {$ r $} is the radius. Consider the following polyhedron

The data of the ball can be computed by calling

where the field x represents the center and r is the radius. The field exitflag is the status returned from the optimization solver. As the ball is a convex set, it can be represented as YSet object and plotted together with the polyhedron

Computation of the Chebyshev center corresponds to inscribing a circle inside a polyhedron which is implemented in the chebyCenter method. The circle is described as {$ \{ x ~|~ \| x-x_c \|_2 \le r \} $} where {$x_c$} is the center and {$ r $} is the radius. Consider the following polyhedron

Computation of the Chebyshev center corresponds to inscribing a circle inside a polyhedron which is implemented in the chebyCenter method. The circle is described as {$ x | \| x-x_c \|_2 \le r $} where {$x_c$} is the center and {$ r $} is the radius. Consider the following polyhedron

To test wheter the polyhedron P contains the origin x0 = [0; 0] , the function contains is invoked

The answer is yes, so the point {$x_0$} is contained inside the polyhedron. What about the point x1 = [3; 0] ?

Computation of the Chebyshev center corresponds to inscribing a circle inside a polyhedron which is implemented in the chebyCenter method. The circle is described as ${ x | \| x-x_c \|_2 \le r $} where {$x_c$} is the center and {$ r $} is the radius. Consider the following polyhedron

 P5 = Polyhedron([ 1.8  -4.8; -7.2 -3.4; -4.2 1.2; 5.8  2.7]);

The data of the circle can be computed by calling

data = P5.chebyCenter()

data = 

    exitflag: 1
           x: [2x1 double]
           r: 3.1497

where the field x represents the center and r is the radius. The field exitflag is the status returned from the optimization solver. As the circle is a convex set, it can be represented as YSet object and plotted together with the polyhedron

 x = sdpvar(2,1);
 S = YSet(x, norm(x - data.x) <= data.r);
 P5.plot('wire', true, 'linewidth', 2);
 hold on
 S.plot('color', 'lightgreen');
 plot(data.x(1), data.x(2), 'ko', 'MarkerSize', 10, 'MarkerFaceColor', 'k');

Note that conversion between H- and V-representations is numerically expensive and can become time-consuming for polyhedra with large number of facets or vertices. The numerical computation is delegated to an external CDD solver that should be present at the installation path. ( If not, type tbxmanager install cddmex to install it.)

The Polyhedron class implements the contains method which tests whether a point (or a set of points) is contained inside a polyhedron. Consider the following polyhedron in V-representation

V = [ -1.7 -0.4; -0.4  0.7; 1.2 -0.8; 0 0.8; 1.3 0.9; -0.3 0.6];
P4 = Polyhedron(V);

To test wheter the polyhedron P contains the origin {$x_0 = [0; 0] $}, the function contains is invoked

x0 = [0; 0];
P4.contains( x0 )

ans =

     1

The answer is yes, so the point {$x_0$} is contained inside the polyhedron. What about the point {$x_1 = [3; 0] $}?

x1 = [3; 0];
P4.contains( x1 )

ans =

     0

The test containment for the points {$x_0$} and {$x_1$} is visualized in the figure below.

• H-representation (by inequalities and equalities)

• V-representation (by vertices and rays)

which can be converted vice-versa. The conversion from H- to V-representation is achieved via computeVRep method. Consider the following polyhedron which is created in H-representation

P2 = Polyhedron('lb', [-1; -2], 'ub', [3; 4])
Polyhedron in R^2 with representations:
    H-rep (redundant)   : Inequalities   4 | Equalities   0
    V-rep               : Unknown (call computeVRep() to compute)
Functions : none

P2.hasVRep

ans =

     0

To compute the V-representation, the method computeVRep method is called

P2.computeVRep
Polyhedron in R^2 with representations:
    H-rep (redundant)   : Inequalities   4 | Equalities   0
    V-rep (redundant)   : Vertices   4 | Rays   0
Functions : none

To obtain the vertices and rays, one has to refer to V and R properties

P2.V

ans =

     3    -2
    -1    -2
    -1     4
     3     4

P2.R

ans =

   Empty matrix: 0-by-2

Note that the method computeVRep is executed automatically when queried for vertices or rays using V or R properties.

The conversion from V- to H-representation is achieved via computeHRep method. Consider the following triangle

 P3 = Polyhedron([4, -1; 4, 5; 8, 3])
Polyhedron in R^2 with representations:
    H-rep               : Unknown (call computeHRep() to compute)
    V-rep (redundant)   : Vertices   3 | Rays   0
Functions : none

P3.hasHRep

ans =

     0

that comprises of three vertices. The H-representation can be computed by

P3.computeHRep
Polyhedron in R^2 with representations:
    H-rep (redundant)   : Inequalities   3 | Equalities   0
    V-rep (redundant)   : Vertices   3 | Rays   0
Functions : none

and the facets can be extracted by pointing to H, He properties (or A, Ae, b, be)

P3.H

ans =

    1.0000    2.0000   14.0000
   -1.0000         0   -4.0000
    1.0000   -1.0000    5.0000

P3.He

ans =

   Empty matrix: 0-by-3

When queried for facets, the method computeHRep is executed automatically.

help Polyhedron/forEach

Polyhedra in the array can be removed

row_array1(2) = []

The basic methods operate onto arrays

row_array2.isEmptySet

ans =

     0     0

column_array1.isFullDim

ans =

     1
     1

column_array2.isBounded

ans =

     1
     1

If the method cannot be executed on an array, the forEach function can be applied. For instance, to compute the grid function cannot be evaluated on an array and one observes an error

row_array2.grid(5)
Error using ConvexSet/grid (line 22)
This method does not support arrays. Use the forEach() method.

In this case, the forEach method can be applied that executes the function on each polyhedron in the array. For more help on forEach function type

help IterableBehavior

The correct syntax for the above example using grid function is given as

N = 5;
output = row_array2.forEach(@(x) grid(x, N), 'UniformOutput', false)

To create a new copy of a Polyhedron object, or an array of polyhedra, the method copy must be invoked otherwise the new object point to the same data, i.e.

Qnew = Q.copy()

Polyhedra can be grouped into column or row arrays. For this purpose in MPT there exist overloaded horzcat and vertcat operators or [ ] brackets. Consider the two following polyhedra

Q = Polyhedron('V', [1, -1; 0, -4; 2.5 -3]);
R = Polyhedron('V', [2.5, -3; -1, 4; 1, -1; -1, 2]);

The row array of polyhedra Q, R can be created twofold

row_array1 = [Q, R]
row_array2 = horzcat(Q, R)

Similarly, the column array is created as

column_array1 = [Q; R]
column_array2 = vertcat(Q, R)

Each polyhedron in the array can be extracted using indices, e.g.

row_array1(1)
column_array1(2)

P1.isEmptySet()

P1.isFullDim()

 P1.isBounded()

 P1.plot()

• Facet and vertex enumeration

The Polyhedron class contains methods that are very useful for determining the basic properties such as

• emptyness - isEmptySet
• boundedness - isBounded
• full-dimensionality - isFullDim

As an example, consider the lower-dimensional polyhedron in the dimension 3

P1 = Polyhedron( 'A', [1 -2.1 -0.5; 0.8 -3.1 0.9; -1.2 0.4 -0.8], 'b', [1; 4.9; -1.8], 'Ae', [1 -0.1 0], 'be', 3)

To query, if the set is empty or nor, use

P1.isEmptySet

ans =

     0

From the construction it is evident that the polyhedron is lower-dimensional because it contains one equality constraint. This fact can be checked using the method

P1.isFullDim

ans =

     0

Boundedness property can be queried by

 P1.isBounded

ans =

     0

method. One can plot the polyhedron to verify that the above properties are true

Facet and vertex enumeration

The Polyhedron object can be created in two forms

• H-representation

• V-representation

Chebyshev center

The Polyhedron object can be created in two forms

• H-representation

Attach:polyhedronHrep.png Δ

• V-representation

Attach:polyhedronVrep.png Δ

• Methods for determining basic properties
• Working with multiple polyhedra
• Geometric methods

Methods for determining basic properties

Working with multiple polyhedra

Geometric methods

Set containment

Chebyshev center

Back to Computational Geometry overview.

Geometric operations with polyhedra

• Methods for determining basic properties
• Working with multiple polyhedra
• Geometric methods
  • Set containment
  • Chebyshev center

Back to computational geometry overview.