Quick Tutorial

Consider a constrained least squares problem

with variable $x\in \mathbf{R}^{n}$, and problem data $A \in \mathbf{R}^{m \times n}$, $b \in \mathbf{R}^{m}$.

This problem can be solved in Convex.jl as follows: :

julia> # Make the Convex.jl module available
       using Convex, SCS

julia> # Generate random problem data
       m = 4;  n = 5
5

julia> A = randn(m, n); b = randn(m, 1)
4×1 Array{Float64,2}:
 -0.5982561974146283
  1.8285635154246862
  0.7473287766124718
  1.9114497945229456

julia> # Create a (column vector) variable of size n x 1.
       x = Variable(n)
Variable
size: (5, 1)
sign: real
vexity: affine
id: 140…713

julia> # The problem is to minimize ||Ax - b||^2 subject to x >= 0
       # This can be done by: minimize(objective, constraints)
       problem = minimize(sumsquares(A * x - b), [x >= 0])
minimize
└─ qol_elem (convex; positive)
   ├─ norm2 (convex; positive)
   │  └─ + (affine; real)
   │     ├─ …
   │     └─ …
   └─ [1.0]
subject to
└─ >= constraint (affine)
   ├─ 5-element real variable (id: 140…713)
   └─ 0

status: `solve!` not called yet

julia> # Solve the problem by calling solve!
       solve!(problem, () -> SCS.Optimizer(verbose=false))

julia> # Check the status of the problem
       problem.status # :Optimal, :Infeasible, :Unbounded etc.
OPTIMAL::TerminationStatusCode = 1

julia> # Get the optimum value
       problem.optval
7.7918849978570375