pulsipher
commented
1 year ago Here are some typical NLP test cases.
using InfiniteOpt, Distributions
function optimal_control()
γ, β, N, = 0.303, 0.727, 1e5
y0 = Dict(:s => 1 - 1/N, :e => 1/N, :i => 0, :r => 0)
model = InfiniteModel()
@infinite_parameter(model, t ∈ [0, 200], num_supports = 201)
@infinite_parameter(model, ξ ~ Uniform(0.1, 0.6), num_supports = 100)
@variable(model, y[keys(y0)] ≥ 0, Infinite(t, ξ))
@variable(model, 0 ≤ u ≤ 0.8, Infinite(t), start = 0.2)
set_upper_bound(y[:i], 0.02)
@objective(model, Min, ∫(u, t))
@constraint(model, [k in keys(y0)], y[k](0, ξ) == y0[k])
@constraint(model, ∂(y[:s], t) == (u - 1) * β * y[:s] * y[:i])
@constraint(model, ∂(y[:e], t) == (1 - u) * β * y[:s] * y[:i] - ξ * y[:e])
@constraint(model, ∂(y[:i], t) == ξ * y[:e] - γ * y[:i])
@constraint(model, ∂(y[:r], t) == γ * y[:i])
build_optimizer_model!(model, check_support_dims = false)
end
function nested_measures()
model = InfiniteModel()
@infinite_parameter(model, x[1:3] in [-1, 1], independent = true, num_supports = 10)
@variable(model, y, Infinite(x))
@variable(model, q, Infinite(x[1]))
@variable(model, z[1:1000])
@objective(model, Min, sum(z) + ∫(sin(q) / q + ∫(∫(log(y^3.6 / cos(y)) + y^2 + y, x[3]), x[2]), x[1]))
@constraint(model, [i in 1:1000], z[i] / q - exp(sin(y)) <= 34)
build_optimizer_model!(model)
end
function large_expressions()
model = InfiniteModel()
@infinite_parameter(model, t in [0, 1], num_supports = 10)
@variable(model, y[1:50000], Infinite(t))
@variable(model, z[1:50000])
@objective(model, Max, sum(2z[i]^2 + ∫(sin(1/y[i]), t) for i = 1:50000))
@constraint(model, prod(ifelse(z[i] <= y[i]^3, log(y[i] / i), z[i] / cos(y[i])) for i in 1:50000) <= 42)
@constraint(model, sum(z[i]^i + log(y[i]) for i in 1:50000) == 0)
build_optimizer_model!(model)
end
@time optimal_control()
@time nested_measures()
@time large_expressions()With this pull request and the most current version of https://github.com/jump-dev/JuMP.jl/pull/3106, I get:
9.274992 seconds (46.83 M allocations: 2.204 GiB, 10.96% gc time, 48.40% compilation time)
20.659800 seconds (137.31 M allocations: 6.034 GiB, 47.38% gc time)
41.839613 seconds (225.99 M allocations: 10.678 GiB, 39.41% gc time)With the InfiniteOpt v0.5.7 and JuMP v1.11.0, I get:
7.100326 seconds (38.98 M allocations: 2.076 GiB, 13.08% gc time)
46.806975 seconds (184.33 M allocations: 10.240 GiB, 16.18% gc time)
ERROR: StackOverflowError:So for problems with lot's of short nonlinear expressions, the master appears to do better. Otherwise, expressions with lots of summation terms do better with this pull request. Moreover, deeply nested expressions lead to StackOverflowErrors in the current version because it uses recursion.
I wonder if we could do even better by more efficiently handling sums and prods to only use only one head and args array for all the terms.
odow
codecov[bot]
This incorporates https://github.com/jump-dev/JuMP.jl/pull/3106.
TODO list: