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ConicIP

Interior-point solver

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ConicIP.jl: A Pure Julia Conic QP Solver

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ConicIP (IP stands for Interior Point, not Integer Programming) is an interior point solver inspired by cvxopt for quadratic programs with polyhedral (here denoted 𝑅) and second order cone (denoted 𝑄) constraints. Since ConicIP is written in Julia, it allows abstract input and allows callbacks for it's most computationaly intensive internal routines.

Basic Usage

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 𝐾

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
s.t.        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.

Usage with modelling libraries

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

JuMP.jl

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]

Convex.jl

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]
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First Commit

03/04/2016

Last Touched

about 2 months ago

Commits

95 commits

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