ADNLPModel(f, x0)
ADNLPModel(f, x0, lvar, uvar)
ADNLPModel(f, x0, clinrows, clincols, clinvals, lcon, ucon)
ADNLPModel(f, x0, A, lcon, ucon)
ADNLPModel(f, x0, c, lcon, ucon)
ADNLPModel(f, x0, clinrows, clincols, clinvals, c, lcon, ucon)
ADNLPModel(f, x0, A, c, lcon, ucon)
ADNLPModel(f, x0, lvar, uvar, clinrows, clincols, clinvals, lcon, ucon)
ADNLPModel(f, x0, lvar, uvar, A, lcon, ucon)
ADNLPModel(f, x0, lvar, uvar, c, lcon, ucon)
ADNLPModel(f, x0, lvar, uvar, clinrows, clincols, clinvals, c, lcon, ucon)
ADNLPModel(f, x0, lvar, uvar, A, c, lcon, ucon)

ADNLPModel is an AbstractNLPModel using automatic differentiation to compute the derivatives. The problem is defined as

 min  f(x)
s.to  lcon ≤ (  Ax  ) ≤ ucon
             ( c(x) )
      lvar ≤   x  ≤ uvar.

The following keyword arguments are available to all constructors:

• minimize: A boolean indicating whether this is a minimization problem (default: true)
• name: The name of the model (default: "Generic")

The following keyword arguments are available to the constructors for constrained problems:

• y0: An inital estimate to the Lagrangian multipliers (default: zeros)

ADNLPModel uses ForwardDiff and ReverseDiff for the automatic differentiation. One can specify a new backend with the keyword arguments backend::ADNLPModels.ADBackend. There are three pre-coded backends:

• the default ForwardDiffAD.
• ReverseDiffAD.
• ZygoteDiffAD accessible after loading Zygote.jl in your environment.

For an advanced usage, one can define its own backend and redefine the API as done in ADNLPModels.jl/src/forward.jl.

Examples

using ADNLPModels
f(x) = sum(x)
x0 = ones(3)
nvar = 3
ADNLPModel(f, x0) # uses the default ForwardDiffAD backend.
ADNLPModel(f, x0; backend = ADNLPModels.ReverseDiffAD) # uses ReverseDiffAD backend.

using Zygote
ADNLPModel(f, x0; backend = ADNLPModels.ZygoteAD)

using ADNLPModels
f(x) = sum(x)
x0 = ones(3)
c(x) = [1x[1] + x[2]; x[2]]
nvar, ncon = 3, 2
ADNLPModel(f, x0, c, zeros(ncon), zeros(ncon)) # uses the default ForwardDiffAD backend.
ADNLPModel(f, x0, c, zeros(ncon), zeros(ncon); backend = ADNLPModels.ReverseDiffAD) # uses ReverseDiffAD backend.

using Zygote
ADNLPModel(f, x0, c, zeros(ncon), zeros(ncon); backend = ADNLPModels.ZygoteAD)

For in-place constraints function, use one of the following constructors:

ADNLPModel!(f, x0, c!, lcon, ucon)
ADNLPModel!(f, x0, clinrows, clincols, clinvals, c!, lcon, ucon)
ADNLPModel!(f, x0, A, c!, lcon, ucon)
ADNLPModel(f, x0, lvar, uvar, c!, lcon, ucon)
ADNLPModel(f, x0, lvar, uvar, clinrows, clincols, clinvals, c!, lcon, ucon)
ADNLPModel(f, x0, lvar, uvar, A, c!, lcon, ucon)

where the constraint function has the signature c!(output, input).

using ADNLPModels
f(x) = sum(x)
x0 = ones(3)
function c!(output, x) 
  output[1] = 1x[1] + x[2]
  output[2] = x[2]
end
nvar, ncon = 3, 2
nlp = ADNLPModel!(f, x0, c!, zeros(ncon), zeros(ncon)) # uses the default ForwardDiffAD backend.

ADNLSModel(F, x0, nequ)
ADNLSModel(F, x0, nequ, lvar, uvar)
ADNLSModel(F, x0, nequ, clinrows, clincols, clinvals, lcon, ucon)
ADNLSModel(F, x0, nequ, A, lcon, ucon)
ADNLSModel(F, x0, nequ, c, lcon, ucon)
ADNLSModel(F, x0, nequ, clinrows, clincols, clinvals, c, lcon, ucon)
ADNLSModel(F, x0, nequ, A, c, lcon, ucon)
ADNLSModel(F, x0, nequ, lvar, uvar, clinrows, clincols, clinvals, lcon, ucon)
ADNLSModel(F, x0, nequ, lvar, uvar, A, lcon, ucon)
ADNLSModel(F, x0, nequ, lvar, uvar, c, lcon, ucon)
ADNLSModel(F, x0, nequ, lvar, uvar, clinrows, clincols, clinvals, c, lcon, ucon)
ADNLSModel(F, x0, nequ, lvar, uvar, A, c, lcon, ucon)

ADNLSModel is an Nonlinear Least Squares model using automatic differentiation to compute the derivatives. The problem is defined as

 min  ½‖F(x)‖²
s.to  lcon ≤ (  Ax  ) ≤ ucon
             ( c(x) )
      lvar ≤   x  ≤ uvar

where nequ is the size of the vector F(x) and the linear constraints come first.

The following keyword arguments are available to all constructors:

• linequ: An array of indexes of the linear equations (default: Int[])
• minimize: A boolean indicating whether this is a minimization problem (default: true)
• name: The name of the model (default: "Generic")

The following keyword arguments are available to the constructors for constrained problems:

• y0: An inital estimate to the Lagrangian multipliers (default: zeros)

ADNLSModel uses ForwardDiff and ReverseDiff for the automatic differentiation. One can specify a new backend with the keyword arguments backend::ADNLPModels.ADBackend. There are three pre-coded backends:

• the default ForwardDiffAD.
• ReverseDiffAD.
• ZygoteDiffAD accessible after loading Zygote.jl in your environment.

For an advanced usage, one can define its own backend and redefine the API as done in ADNLPModels.jl/src/forward.jl.

Examples

using ADNLPModels
F(x) = [x[2]; x[1]]
nequ = 2
x0 = ones(3)
nvar = 3
ADNLSModel(F, x0, nequ) # uses the default ForwardDiffAD backend.
ADNLSModel(F, x0, nequ; backend = ADNLPModels.ReverseDiffAD) # uses ReverseDiffAD backend.

using Zygote
ADNLSModel(F, x0, nequ; backend = ADNLPModels.ZygoteAD)

using ADNLPModels
F(x) = [x[2]; x[1]]
nequ = 2
x0 = ones(3)
c(x) = [1x[1] + x[2]; x[2]]
nvar, ncon = 3, 2
ADNLSModel(F, x0, nequ, c, zeros(ncon), zeros(ncon)) # uses the default ForwardDiffAD backend.
ADNLSModel(F, x0, nequ, c, zeros(ncon), zeros(ncon); backend = ADNLPModels.ReverseDiffAD) # uses ReverseDiffAD backend.

using Zygote
ADNLSModel(F, x0, nequ, c, zeros(ncon), zeros(ncon); backend = ADNLPModels.ZygoteAD)

For in-place constraints and residual function, use one of the following constructors:

ADNLSModel!(F!, x0, nequ)
ADNLSModel!(F!, x0, nequ, lvar, uvar)
ADNLSModel!(F!, x0, nequ, c!, lcon, ucon)
ADNLSModel!(F!, x0, nequ, clinrows, clincols, clinvals, c!, lcon, ucon)
ADNLSModel!(F!, x0, nequ, A, c!, lcon, ucon)
ADNLSModel!(F!, x0, nequ, lvar, uvar, c!, lcon, ucon)
ADNLSModel!(F!, x0, nequ, lvar, uvar, clinrows, clincols, clinvals, c!, lcon, ucon)
ADNLSModel!(F!, x0, nequ, lvar, uvar, A, c!, lcon, ucon)

where the constraint function has the signature c!(output, input).

using ADNLPModels
function F!(output, x)
  output[1] = x[2]
  output[2] = x[1]
end
nequ = 2
x0 = ones(3)
function c!(output, x) 
  output[1] = 1x[1] + x[2]
  output[2] = x[2]
end
nvar, ncon = 3, 2
nls = ADNLSModel!(F!, x0, nequ, c!, zeros(ncon), zeros(ncon))