FirstOrderSolvers.jl
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mfalt
Large scale convex optimization solvers in julia
FirstOrderSolvers
Package for large scale convex optimization solvers in julia. This package is intended to allow for easy implementation, testing, and running of solvers through the Convex.jl interface. The package is currently under active development and uses the ProximalOperators.jl package to do the low level projections.
Installation
To run the solvers you need to have the following packages
Pkg.add("Convex")
Pkg.clone("https://github.com/mfalt/FirstOrderSolvers.jl.git")
Usage
Define an optimization problem in the format supported by Convex.jl, and supply the desired solver to the solve! function. Exaple using DR for feasibility problems with the GAP solver
using Convex, FirstOrderSolvers
m = 40; n = 50
A = randn(m, n); b = randn(m, 1)
x = Variable(n)
problem = minimize(sumsquares(A * x - b), [x >= 0])
solve!(problem, GAP(0.5, 2.0, 2.0, max_iters=2000))
Solvers
Currently, the available solvers are
| Solver | Description | Reference |
|---|---|---|
GAP(α=0.8, α1=1.8, α2=1.8; kwargs...) |
Generalized Alternating Projections | |
DR(α=0.5; kwargs...) |
Douglas-Rachford (GAP(α, 2.0, 2.0)) |
Douglas, Rachford (1956) |
AP(α=0.5; kwargs...) |
Alternating Projections (GAP(α, 1.0, 1.0)) |
Agmon (1954), Bregman (1967) |
GAPA(α=1.0; kwargs...) |
GAP Adaptive | Fält, Giselsson (2017) |
FISTA(α=1.0; kwargs...) |
FISTA | Beck, Teboulle (2009) |
Dykstra(; kwargs...) |
Dykstra | Boyle, Dykstra (1986) |
GAPP(α=0.8, α1=1.8, α2=1.8; iproj=100; kwargs...) |
Projected GAP | Fält, Giselsson (2016) |
Keyword Arguments
All solvers accept for the following keyword arguments:
| Argument | Default | Description (Values) |
|---|---|---|
max_iters |
10000 |
Maximum number of iterations |
verbose |
1 |
Print verbosity level 0,1 |
debug |
1 |
Level of debug data to save 0,1,2 |
eps |
1e-5 |
Accuracy of solution |
checki |
100 |
Interval for checking convergence |
Debugging
If the keyword argument debug is set to 1 or 2 the following values will be stored in a ValueHistories.MVHistory in problem.model.history, for each iteration the convergence check is run:
| Name | Debug Level Required | Description |
|---|---|---|
:p |
1 | Relative Primal Residual |
:d |
1 | Relative Dual Residual |
:g |
1 | Relative Duality Gap |
:ctx |
1 | cᵀx |
:bty |
1 | bᵀy |
:κ |
1 | κ |
:τ |
1 | τ |
:x |
2 | x |
:y |
2 | y |
:s |
2 | s |
These values correspond to the values in the paper Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding (O'Donoghue et.al).
mfalt