# How to create a model from the function and its derivatives

When you know the derivatives of your optimization problem, it is frequently more efficient to use them directly instead of relying on automatic differentiation. For that purpose, we have created ManualNLPModels. The package is very crude, due to demand being low, but let us know if you need more functionalities.

For instance, in the logistic regression problem, we have a model $$h_{\beta}(x) = \sigma(\hat{x}^T \beta) = \sigma(\beta_0 + x^T\beta_{1:p})$$, where $$\hat{x} = \begin{bmatrix} 1 \\ x \end{bmatrix}$$. The value of $$\beta$$ is found by finding the minimum of the negavitve of the log-likelihood function.

$\ell(\beta) = -\frac{1}{n} \sum_{i=1}^n y_i \ln \big(h_{\beta}(x_i)\big) + (1 - y_i) \ln\big(1 - h_{\beta}(x_i)\big).$

We'll input the gradient of this function manually. It is given by

$\nabla \ell(\beta) = \frac{-1}{n} \sum_{i=1}^n \big(y_i - h_{\beta}(x_i)\big) \hat{x}_i = \frac{1}{n} \begin{bmatrix} e^T \\ X^T \end{bmatrix} (h_{\beta}(X) - y),$

where $$e$$ is the vector with all components equal to 1.

using ManualNLPModels
using LinearAlgebra, Random
Random.seed!(0)

sigmoid(t) = 1 / (1 + exp(-t))
h(β, X) = sigmoid.(β[1] .+ X * β[2:end])

n, p = 500, 50
X = randn(n, p)
β = randn(p + 1)
y = round.(h(β, X) .+ randn(n) * 0.1)

function myfun(β, X, y)
@views hβ = sigmoid.(β[1] .+ X * β[2:end])
out = sum(
yᵢ * log(ŷᵢ + 1e-8) + (1 - yᵢ) * log(1 - ŷᵢ + 1e-8)
for (yᵢ, ŷᵢ) in zip(y, hβ)
)
return -out / n + 0.5e-4 * norm(β)^2
end

n = length(y)
@views δ = (sigmoid.(β[1] .+ X * β[2:end]) - y) / n
out[1] = sum(δ) + 1e-4β[1]
@views out[2:end] .= X' * δ + 1e-4 * β[2:end]
return out
end

nlp = NLPModel(
zeros(p + 1),
β -> myfun(β, X, y),
)
ManualNLPModels.NLPModel{Float64, Vector{Float64}}
Problem name: Generic
All variables: ████████████████████ 51     All constraints: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅
⋅⋅⋅⋅⋅⋅⋅⋅ 0
free: ████████████████████ 51                free: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅
⋅⋅⋅⋅⋅⋅⋅⋅ 0
lower: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                lower: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅
⋅⋅⋅⋅⋅⋅⋅⋅ 0
upper: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                upper: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅
⋅⋅⋅⋅⋅⋅⋅⋅ 0
low/upp: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0              low/upp: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅
⋅⋅⋅⋅⋅⋅⋅⋅ 0
fixed: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                fixed: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅
⋅⋅⋅⋅⋅⋅⋅⋅ 0
infeas: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0               infeas: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅
⋅⋅⋅⋅⋅⋅⋅⋅ 0
nnzh: (100.00% sparsity)   0               linear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅
⋅⋅⋅⋅⋅⋅⋅⋅ 0
nonlinear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅
⋅⋅⋅⋅⋅⋅⋅⋅ 0
nnzj: (------% spa
rsity)

Counters:
⋅⋅⋅⋅⋅⋅⋅⋅ 0                 cons: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
cons_lin: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0             cons_nln: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅
⋅⋅⋅⋅⋅⋅⋅⋅ 0                 jcon: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
⋅⋅⋅⋅⋅⋅⋅⋅ 0              jac_lin: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
jac_nln: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                jprod: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅
⋅⋅⋅⋅⋅⋅⋅⋅ 0            jprod_lin: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
jprod_nln: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0               jtprod: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅
⋅⋅⋅⋅⋅⋅⋅⋅ 0           jtprod_lin: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
jtprod_nln: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                 hess: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅
⋅⋅⋅⋅⋅⋅⋅⋅ 0                hprod: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
jhess: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0               jhprod: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅
⋅⋅⋅⋅⋅⋅⋅⋅ 0

Notice that the grad function must modify the first argument so you don't waste memory creating arrays.

Only the obj, grad and grad! functions will be defined for this model, so you need to choose your solver carefully. We'll use lbfgs from JSOSolvers.jl.

using JSOSolvers

output = lbfgs(nlp)
βsol = output.solution
ŷ = round.(h(βsol, X))
sum(ŷ .== y) / n
1.0

We can compare against other approaches.

using BenchmarkTools
using Logging

@benchmark begin
nlp = NLPModel(
zeros(p + 1),
β -> myfun(β, X, y),
)
output = with_logger(NullLogger()) do
lbfgs(nlp)
end
end
BenchmarkTools.Trial: 1525 samples with 1 evaluation.
Range (min … max):  2.652 ms …   8.818 ms  ┊ GC (min … max): 0.00% … 55.60
%
Time  (median):     3.086 ms               ┊ GC (median):    0.00%
Time  (mean ± σ):   3.272 ms ± 817.140 μs  ┊ GC (mean ± σ):  4.17% ±  9.81
%

█▆▆▃▂▄▂▂▁
▅█████████▆▄▃▂▃▂▃▃▂▃▂▂▂▂▂▁▂▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▂▂▂▂▂▂▂▂▃▂▂▂▂▂▁▂ ▃
2.65 ms         Histogram: frequency by time        7.36 ms <

Memory estimate: 1.69 MiB, allocs estimate: 1698.
using ADNLPModels

@benchmark begin
output = with_logger(NullLogger()) do
end
end
BenchmarkTools.Trial: 58 samples with 1 evaluation.
Range (min … max):  81.476 ms … 91.982 ms  ┊ GC (min … max): 0.00% … 2.55%
Time  (median):     86.416 ms              ┊ GC (median):    2.64%
Time  (mean ± σ):   86.565 ms ±  2.145 ms  ┊ GC (mean ± σ):  1.88% ± 1.26%

▂▂ ▅     ▅    █   ▂
▅▁▁▁▁▅▅▁▁▅▅▁▁▁▁▁▅▁███▅████▅███▅▁███▁▅█▅▅▁▁▁▅█▅▁▁▁▅▁▁▁▁▁▅▁▁▅ ▁
81.5 ms         Histogram: frequency by time        91.8 ms <

Memory estimate: 30.55 MiB, allocs estimate: 3873.
using JuMP
using NLPModelsJuMP

@benchmark begin
model = Model()
@variable(model, modelβ[1:p+1])
@NLexpression(model,
xᵀβ[i=1:n],
modelβ[1] + sum(modelβ[j + 1] * X[i,j] for j = 1:p)
)
@NLexpression(
model,
hβ[i=1:n],
1 / (1 + exp(-xᵀβ[i]))
)
@NLobjective(model, Min,
-sum(y[i] * log(hβ[i] + 1e-8) + (1 - y[i] * log(hβ[i] + 1e-8)) for i = 1:n) / n + 0.5e-4 * sum(modelβ[i]^2 for i = 1:p+1)
)
jumpnlp = MathOptNLPModel(model)
output = with_logger(NullLogger()) do
lbfgs(jumpnlp)
end
end
BenchmarkTools.Trial: 34 samples with 1 evaluation.
Range (min … max):  139.169 ms … 167.364 ms  ┊ GC (min … max): 12.30% … 10
.29%
Time  (median):     145.889 ms               ┊ GC (median):    12.87%
Time  (mean ± σ):   147.385 ms ±   6.147 ms  ┊ GC (mean ± σ):  12.77% ±  2
.08%

█  █
█▅▁▅▁▁▁▅█▅▁█▁▅█▅██▅▁▅▁▁▁▅▅▁▁▁▁▁▅▁▁▅▁▁▁▅▁▅▁▁▁▁▅▁▁▁▁▁▁▁▁▁▁▁▁▁▁▅ ▁
139 ms           Histogram: frequency by time          167 ms <

Memory estimate: 147.57 MiB, allocs estimate: 2149738.

using NLPModels

@benchmark grad(nlp, β)
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
Range (min … max):  12.401 μs …  5.692 ms  ┊ GC (min … max): 0.00% … 85.16
%
Time  (median):     16.400 μs              ┊ GC (median):    0.00%
Time  (mean ± σ):   18.916 μs ± 89.857 μs  ┊ GC (mean ± σ):  7.24% ±  1.56
%

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12.4 μs         Histogram: frequency by time        33.2 μs <

Memory estimate: 18.19 KiB, allocs estimate: 8.
adnlp = ADNLPModel(β -> myfun(β, X, y), zeros(p + 1))
@benchmark grad(adnlp, β)
BenchmarkTools.Trial: 3902 samples with 1 evaluation.
Range (min … max):  1.121 ms …   4.791 ms  ┊ GC (min … max): 0.00% … 67.08
%
Time  (median):     1.243 ms               ┊ GC (median):    0.00%
Time  (mean ± σ):   1.278 ms ± 276.957 μs  ┊ GC (mean ± σ):  1.79% ±  5.93
%

▆█▇▅▃
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1.12 ms         Histogram: frequency by time        1.41 ms <

Memory estimate: 472.88 KiB, allocs estimate: 42.
model = Model()
@variable(model, modelβ[1:p+1])
@NLexpression(model,
xᵀβ[i=1:n],
modelβ[1] + sum(modelβ[j + 1] * X[i,j] for j = 1:p)
)
@NLexpression(
model,
hβ[i=1:n],
1 / (1 + exp(-xᵀβ[i]))
)
@NLobjective(model, Min,
-sum(y[i] * log(hβ[i] + 1e-8) + (1 - y[i] * log(hβ[i] + 1e-8)) for i = 1:n) / n + 0.5e-4 * sum(modelβ[i]^2 for i = 1:p+1)
)
jumpnlp = MathOptNLPModel(model)
@benchmark grad(jumpnlp, β)
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
Range (min … max):  194.703 μs … 871.915 μs  ┊ GC (min … max): 0.00% … 0.0
0%
Time  (median):     205.904 μs               ┊ GC (median):    0.00%
Time  (mean ± σ):   207.619 μs ±  11.843 μs  ┊ GC (mean ± σ):  0.00% ± 0.0
0%

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195 μs           Histogram: frequency by time          231 μs <

Memory estimate: 496 bytes, allocs estimate: 1.

Take these benchmarks with a grain of salt. They are being run on a github actions server with global variables. If you want to make an informed option, you should consider performing your own benchmarks.