Arbitrary Precision

COSMO allows you to solve problems with arbitrary floating-point precision, e.g. by using BigFloat problem data. To do this, the desired floating point type has to be consistent across the model COSMO.Model{<: AbstractFloat}, the input data and the (optional) settings object COSMO.Settings{<: AbstractFloat}. As an example, assume we want to solve the following quadratic program:

\[\begin{array}{ll} \text{minimize} & 1/2 x^\top P x + q^\top x \\ \text{subject to} & l \leq A x \leq u \end{array}\]

where $P = \begin{bmatrix} 4 & 1 \\ 1 & 2\end{bmatrix}$, $q = [1, 1]^\top$, $A = \begin{bmatrix} 1 & 1 \\ 1 & 0 \\ 0 & 1\end{bmatrix}$ and $l= [1,0, 0]^\top$, $u=[1, 0.7, 0.7]^\top$. We start by creating the model with the desired precision:

using COSMO, LinearAlgebra, SparseArrays
model = COSMO.Model{BigFloat}()

A COSMO Model with Float precision: BigFloat

Next, we define the problem data as BigFloat arrays and create the constraint:

q = BigFloat[1; 1.]
P = sparse(BigFloat[4. 1; 1 2])
A = BigFloat[1. 1; 1 0; 0 1]
l = BigFloat[1.; 0; 0]
u = BigFloat[1; 0.7; 0.7]

constraint = COSMO.Constraint(A, zeros(BigFloat, 3), COSMO.Box(l, u))

Constraint{BigFloat}
Size of A: (3, 2)
ConvexSet: COSMO.Box{BigFloat}

Notice that the constraint type parameter is dependent on the input data. The same is true for the constraint set Box. Next, we define the settings

settings = COSMO.Settings{BigFloat}(verbose = true, kkt_solver = QdldlKKTSolver)

A COSMO.Settings{BigFloat} object. To list the available options type `?` and `help?>COSMO.Settings`.

and assemble and solve the problem:

assemble!(model, P, q, constraint, settings = settings)
result = COSMO.optimize!(model);

------------------------------------------------------------------
          COSMO v0.8.9 - A Quadratic Objective Conic Solver
                         Michael Garstka
                University of Oxford, 2017 - 2022
------------------------------------------------------------------

Problem:  x ∈ R^{2},
          constraints: A ∈ R^{3x2} (4 nnz),
          matrix size to factor: 5x5,
          Floating-point precision: BigFloat
Sets:     Box of dim: 3
Settings: ϵ_abs = 1.0e-05, ϵ_rel = 1.0e-05,
          ϵ_prim_inf = 1.0e-04, ϵ_dual_inf = 1.0e-04,
          ρ = 0.1, σ = 1e-06, α = 1.6,
          max_iter = 5000,
          scaling iter = 10 (on),
          check termination every 25 iter,
          check infeasibility every 40 iter,
          KKT system solver: QDLDL
Acc:      Anderson Type2{QRDecomp},
          Memory size = 5, RestartedMemory,
          Safeguarded: true, tol: 2.0
Setup Time: 1165.5ms

Iter:	Objective:	Primal Res:	Dual Res:	Rho:
1	-7.8808e-03	1.0079e+00	2.0033e+02	1.0000e-01
25	 1.8800e+00	1.2972e-77	7.3280e-77	1.0000e-01

------------------------------------------------------------------
>>> Results
Status: Solved
Iterations: 25
Optimal objective: 1.88
Runtime: 3.289s (3289.4ms)

Moreover, notice that when no type parameter is specified, all objects default to Float64:

model = COSMO.Model()

A COSMO Model with Float precision: Float64

Two limitations to arbitrary precision:

Since we call the LAPACK function syevr for eigenvalue decompositions, we currently only support solving problems with PSD constraints in Float32 and Float64.
We suggest to use the pure Julia QDLDL linear system solver (kkt_solver = QdldlKKTSolver) when working with arbitrary precision types as some of the other available solvers don't support all available precisions.

Note

If you want to use COSMO directly with MathOptInterface, you can use: COSMO.Optimizer{<: AbstractFloat} as your optimizer. Again, the problem data precision of your MathOptInterface-model has to agree with the optimizer's precision.

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