# SetProg

Documentation |
Build Status |
Social |
---|---|---|

JuMP extension for Set Programming : optimization with set variables and inclusion/containment constraints. This package allows the formulation of a mathematical programming involving both classical variables and constraints supported by JuMP and set variables and constraints.

## Documentation

**STABLE**—**most recently tagged version of the documentation.****LATEST**—*in-development version of the documentation.*

## Variables

The variables can either be

- a
`Polytope`

; - an
`Ellipsoid`

, or a piecewise semi-ellipsoid; - a
`Polyset`

, that is the 1-sublevel set of a polynomial of degree`2d`

.

```
@variable model S Polytope(piecewise=p) # polytope defined over the pieces defined by `p`
@variable model S Ellipsoid()
@variable model S Ellipsoid(piecewise=p) # piecewise semi-ellipsoid defined over the pieces defined by `p`
@variable model S PolySet(d) # 1-sublevel set of a polynomial of degree 2d
@variable model S PolySet(d, convex=true) # Convex 1-sublevel set of a polynomial of degree 2d
@variable model S PolySet(d, symmetric=true) # 1-sublevel set of a polynomial of degree 2d symmetric around the origin
@variable model S PolySet(d, symmetric=true, point=SetProg.CenterPoint([1, 0])) # 1-sublevel set of a polynomial of degree 2d symmetric around the [1, 0]
```

## Expressions

The following operations are allowed:

Operation | Description |
---|---|

A*S | Linear mapping |

But more operations are planned to be added:

Operation | Description |
---|---|

S + x | Translation of `S` by `x` |

S1 + S2 | Minkowski sum |

S1 ∩ S2 | Intersection of `S1` and `S2` |

S1 ∪ S2 | Union of `S1` and `S2` |

polar(S) | Polar of S |

## Constraints

The following constraints are implemented

Operation | Description |
---|---|

x ∈ S | `x` is contained in `S` |

S1 ⊆ S2 | `S1` is included in `S2` |

S1 ⊇ S2 | `S1` is included in `S2` |

## Examples

Consider a polytope

```
using Polyhedra
diamond = HalfSpace([1, 1], 1) ∩ HalfSpace([-1, -1], 1) ∩ HalfSpace([1, -1], 1) ∩ HalfSpace([-1, 1], 1)
simplex = HalfSpace([1, 1], 1) ∩ HalfSpace([-1, 0], 0) ∩ HalfSpace([0, -1], 0)
```

Pick an SDP solver (see here for a list)

```
using CSDP # Optimizer
optimizer_constructor = CSDP.Optimizer
```

To compute the maximal symmetric ellipsoid contained in the polytope `diamond`

defined above (i.e. Löwner-John ellipsoid):

```
using SetProg
model = Model(optimizer_constructor)
@variable(model, S, Ellipsoid(symmetric=true))
@constraint(model, S ⊆ diamond)
@objective(model, Max, nth_root(volume(S)))
optimize!(model)
```

We specify in the example that the ellipsoid is symmetric around the origin to simplify the computation as the solver does not need to look for the center so the SDP problem that need to be solved has a smaller size.

We can visualize the result with Plots as follows:

```
using Plots
plot(polyhedron(diamond), ratio=1)
plot!(value(S))
```

To compute the maximal ellipsoid contained in `simplex`

, we don't need to specify
the center but at least a point in the interior of the ellipsoid. The SDP
formulation used will then determine the center and shape of the ellipsoid
simultaneously in the same SDP. For the interior point, we take the chebyshev
center of the simplex (which can be found by solving an LP). This the center of
the sphere of maximal volume in the simplex so one might rightly guess that is is
in the interior of the maximal ellispoid contained in the simplex.

```
using SetProg
cheby_center, cheby_radius = chebyshevcenter(simplex, optimizer_constructor)
interior_point = SetProg.InteriorPoint(cheby_center)
model = Model(optimizer_constructor)
@variable(model, S, Ellipsoid(point=interior_point))
@constraint(model, S ⊆ simplex)
@objective(model, Max, nth_root(volume(S)))
optimize!(model)
```

We now visualize the result:

```
using Plots
plot(polyhedron(simplex), ratio=1)
plot!(value(S))
```

To compute the maximal invariant set contained in a polytope (*not yet implemented*):

```
using SetProg
model = Model(optimizer_constructor)
@variable(model, S, Polyhedron())
@constraint(model, S ⊆ diamond)
@constraint(model, A*S ⊆ S) # Invariance constraint
@objective(model, Max, volume(S))
optimize!(model)
```

To compute the maximal invariant ellipsoid contained in the polytope `diamond`

defined above:

```
using SetProg
model = Model(optimizer_constructor)
@variable(model, S, Ellipsoid(symmetric=true))
@constraint(model, S ⊆ diamond)
@constraint(model, A*S ⊆ S) # Invariance constraint
@objective(model, Max, nth_root(volume(S)))
optimize!(model)
```

To compute the maximal algebraic-invariant ellipsoid (i.e. `AS ⊆ ES`

) contained in the polytope `diamond`

defined above:

```
using SetProg
model = Model(optimizer_constructor)
@variable(model, S, Ellipsoid(symmetric=true)))
@constraint(model, S ⊆ diamond)
@constraint(model, A*S ⊆ E*S) # Invariance constraint
@objective(model, Max, L1_heuristic(volume(S), ones(Polyhedra.fulldim(P))))
optimize!(model)
```