`ConicIP`

(Conic **I**nterior **P**oint) is an interior-point solver inspired by cvxopt for optimizing quadratic objectives with linear equality constraints, and polyhedral, second-order cone constraints. (Semidefinite cone constraints are available, but only supported as an experimental feature.) Because ConicIP is written in Julia, it allows abstract input and allows callbacks for its most computationaly intensive internal routines.

ConicIP has the interface

```
sol = conicIP( Q , c , A , b , 𝐾 , G , d )
```

For the problem

```
minimize ½yᵀQy - cᵀy
s.t Ay ≧𝐾 b, 𝐾 = 𝐾₁ × ⋯ × 𝐾ⱼ
Gy = d
```

`𝐾`

is a list of tuples of the form `(Cone Type ∈ {"R", "Q"}, Cone Dimension)`

specifying the cone `𝐾ᵢ`

. For example, the cone `𝐾 = 𝑅² × 𝑄³ × 𝑅²`

has the following specification:

```
𝐾 = [ ("R",2) , ("Q",3), ("R",2) ]
```

ConicIP returns `sol`

, a structure containing error information (`sol.status`

), the primal variables (`sol.y`

), dual variables (`sol.v`

, `sol.w`

), and convergence information.

To solve the problem

```
minimize ½yᵀQy - cᵀy
such that y ≧ 0
```

for example, use `ConicIP`

as follows

```
using ConicIP
n = 1000
Q = sparse(randn(n,n))
Q = Q'*Q
c = ones(n,1)
A = speye(n)
b = zeros(n,1)
𝐾 = [("R",n)]
sol = conicIP(Q, c, A, b, 𝐾, verbose=true);
```

For a more detailed example involving callback functions, refer to this notebook.

ConicIP is integrated with MathProgBase and can be used as a solver in JuMP and Convex.

```
using JuMP
using ConicIP
m = Model(solver = ConicIPSolver())
@variable(m, x[1:10] >= 0)
@constraint(m, sum(x) == 1.0)
@objective(m, Min, sum(x))
status = solve(m)
getvalue(x) # should be [0.1 0.1 ⋯ 0.1]
```

**Note:** JuMP does not currently allow mixing quadratic objectives with conic constraints.

```
using Convex
using ConicIP
set_default_solver(ConicIPSolver())
x = Variable(10)
p = minimize( sum(x), [x >= 0, sum(x) == 1])
solve!(p)
x # should be [0.1 0.1 ⋯ 0.1]
```

03/04/2016

about 2 months ago

98 commits