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Add algorithm parameters in kwargs #158

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Apr 5, 2024
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Add algorithm parameters in kwargs #158

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24 changes: 17 additions & 7 deletions src/method.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -88,6 +88,11 @@ For advanced usage, first define a `PercivalSolver` to preallocate the memory us
- `max_iter::Int = 2000`: maximum number of iterations;
- `verbose::Int = 0`: if > 0, display iteration details every `verbose` iteration;
- `μ::Real = T(10.0)`: Starting value of the penalty parameter;
- `η₀::T = T(0.5)`: Starting value for the contraints tolerance of the subproblem;
- `ω₀::T = T(1)`: Starting value for relative tolerance of the subproblem;
- `α₁::T = T(9 // 10)`: ``η = max(1 / al_nlp.μ^α₁, ϵp)`` if ``‖c(xᵏ)‖ ≤ η``;
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Add constraints

- `β₁::T = T(1 // 10)`: ``η = max(1 / al_nlp.μ^β₁, ϵp)`` if ``‖c(xᵏ)‖ > η``;
- `μ_up::T = T(10)`: Multiplicative factor of `μ` if not ``‖c(xᵏ)‖ > η``;
- `subsolver_logger::AbstractLogger = NullLogger()`: logger passed to `tron`;
- `cgls_verbose::Int = 0`: verbosity level in `Krylov.cgls`;
- `inity::Bool = false`: If `true` the algorithm uses `Krylov.cgls` to compute an approximation, otherwise we use `nlp.meta.y0`;
Expand Down Expand Up @@ -286,7 +291,12 @@ function SolverCore.solve!(
stats::GenericExecutionStats{T, V};
callback = (args...) -> nothing,
x::V = nlp.meta.x0,
μ::Real = T(10.0),
μ::Real = T(10),
η₀::T = T(1 // 2),
ω₀::T = T(1),
α₁::T = T(9 // 10),
β₁::T = T(1 // 10),
μ_up::T = T(10),
max_iter::Int = 2000,
max_time::Real = 30.0,
max_eval::Int = 200000,
Expand Down Expand Up @@ -328,9 +338,6 @@ function SolverCore.solve!(
y .= nlp.meta.y0
end
set_constraint_multipliers!(stats, y)
# tolerance
η = T(0.5)
ω = T(1.0)

# create initial subproblem
reinit!(al_nlp, nlp, fx, μ, x, y)
Expand All @@ -348,6 +355,9 @@ function SolverCore.solve!(
# tolerance for optimal measure
ϵd = atol + rtol * normgp
ϵp = ctol
# tolerance
η = max(η₀, ϵp)
ω = ω₀

set_iter!(stats, 0)
start_time = time()
Expand Down Expand Up @@ -412,13 +422,13 @@ function SolverCore.solve!(

iter_type = if normcx <= η
update_y!(al_nlp)
η = max(η / al_nlp.μ^T(0.9), ϵp)
η = max(η / al_nlp.μ^α₁, ϵp)
ω /= al_nlp.μ
set_constraint_multipliers!(stats, al_nlp.y)
:update_y
else
update_μ!(al_nlp, 10 * al_nlp.μ)
η = max(1 / al_nlp.μ^T(0.1), ϵp)
update_μ!(al_nlp, μ_up * al_nlp.μ)
η = max(1 / al_nlp.μ^β₁, ϵp)
ω = 1 / al_nlp.μ
:update_μ
end
Expand Down
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