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arnavk23tmigot
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tmigot
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Add lin_nnzj and nln_nnzj and docstrings for functions (#500)
* Update api.jl * Update show.jl * Update show.jl * Update show.jl * Update show.jl * Update show.jl * Update show.jl * Update show.jl * Update show.jl * Update show.jl * Update show.jl * Update show.jl * Update show.jl * Update show.jl * Update show.jl * Update show.jl * Update show.jl * Update src/nlp/show.jl Co-authored-by: Tangi Migot <[email protected]> * Update src/nlp/show.jl Co-authored-by: Tangi Migot <[email protected]> * Update show.jl * Update show.jl * Update show.jl * Update README.md * Update README.md * Update show.jl * Update show.jl * Update show.jl * Update show.jl * Update README.md * Update README.md * Update show.jl * Update show.jl * Update src/nlp/show.jl Co-authored-by: Tangi Migot <[email protected]> * Update src/nlp/show.jl Co-authored-by: Tangi Migot <[email protected]> * Update show.jl * Update show.jl * Update show.jl * Update show.jl * Update show.jl * Update show.jl --------- Co-authored-by: Tangi Migot <[email protected]>
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‎README.md‎

Lines changed: 3 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -111,8 +111,10 @@ Attribute | Type | Notes
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`jfree` | `Vector{Int}` | indices of "free" constraints (there shouldn't be any)
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`jinf` | `Vector{Int}` | indices of the visibly infeasible constraints
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`nnzo` | `Int ` | number of nonzeros in the gradient
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`nnzj` | `Int ` | number of nonzeros in the sparse Jacobian
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`nnzh` | `Int ` | number of nonzeros in the sparse Hessian
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`nnzj` | `Int ` | number of nonzeros in the sparse Jacobian
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`lin_nnzj` | `Int ` | number of nonzeros in the linear part of sparse Jacobian
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`nln_nnzj` | `Int ` | number of nonzeros in the nonlinear part of sparse Jacobian
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`minimize` | `Bool ` | true if `optimize == minimize`
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`islp` | `Bool ` | true if the problem is a linear program
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`name` | `String` | problem name

‎src/nlp/api.jl‎

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Original file line numberDiff line numberDiff line change
@@ -1344,6 +1344,30 @@ function hess_op!(
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return LinearOperator{T}(nlp.meta.nvar, nlp.meta.nvar, true, true, prod!, prod!, prod!)
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end
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"""
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varscale(model::AbstractNLPModel)
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Return a vector containing the scaling factors for each variable in the model.
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This is typically used to normalize variables for numerical stability in solvers.
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By default, the scaling is model-dependent. If not overridden by the model, a vector of ones
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is returned. Inspired by the AMPL scaling conventions.
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"""
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function varscale end
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"""
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lagscale(model::AbstractNLPModel)
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Return a vector of scaling factors for the Lagrange multipliers associated with constraints.
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This can be used to improve numerical stability or condition number when solving KKT systems.
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"""
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function lagscale end
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"""
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conscale(model::AbstractNLPModel)
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Return a vector of constraint scaling factors for the model.
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These are typically used to normalize constraints to have similar magnitudes and improve
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convergence behavior in nonlinear solvers.
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"""
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function conscale end

‎src/nlp/show.jl‎

Lines changed: 17 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -68,7 +68,7 @@ end
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Describe `meta` for the `show` function.
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"""
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function lines_of_description(m::AbstractNLPModelMeta)
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function lines_of_description(m::M) where {M <: AbstractNLPModelMeta}
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V = [
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length(m.ifree),
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length(m.ilow),
@@ -93,8 +93,22 @@ function lines_of_description(m::AbstractNLPModelMeta)
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V = [sum(V); V]
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S = ["All constraints", "free", "lower", "upper", "low/upp", "fixed", "infeas"]
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conlines = lines_of_hist(S, V)
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push!(conlines, histline("linear", m.nlin, m.ncon), histline("nonlinear", m.nnln, m.ncon))
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push!(conlines, sparsityline("nnzj", m.nnzj, m.nvar * m.ncon))
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append!(
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conlines,
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[
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histline("linear", m.nlin, m.ncon),
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histline("nonlinear", m.nnln, m.ncon),
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sparsityline("nnzj", m.nnzj, m.nvar * m.ncon),
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],
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)
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if :lin_nnzj in fieldnames(M)
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push!(conlines, sparsityline("lin_nnzj", m.lin_nnzj, m.nlin * m.nvar))
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end
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if :nln_nnzj in fieldnames(M)
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push!(conlines, sparsityline("nln_nnzj", m.nln_nnzj, m.nnln * m.nvar))
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end
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append!(varlines, repeat([" "^length(varlines[1])], length(conlines) - length(varlines)))
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lines = varlines .* conlines

‎test/nlp/show.jl‎

Lines changed: 6 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -16,6 +16,8 @@
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nnzh: ( 33.33% sparsity) 2 linear: ██████████⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 1
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nonlinear: ██████████⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 1
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nnzj: ( 0.00% sparsity) 4
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lin_nnzj: ( 0.00% sparsity) 2
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nln_nnzj: ( 0.00% sparsity) 2
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Counters:
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obj: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0 grad: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0 cons: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
@@ -39,7 +41,9 @@
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fixed: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0 fixed: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
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infeas: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0 infeas: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
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nnzh: ( 0.00% sparsity) 1 linear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
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nonlinear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
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nnzj: (------% sparsity)\n\n"""
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nonlinear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
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nnzj: (------% sparsity)
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lin_nnzj: (------% sparsity)
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nln_nnzj: (------% sparsity)\n\n"""
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@test strip.(split(chomp(showed), "\n")) == strip.(split(chomp(expected), "\n"))
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end

‎test/nls/show.jl‎

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@@ -16,6 +16,8 @@
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nnzh: ( 0.00% sparsity) 3 linear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
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nonlinear: ████████████████████ 3
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nnzj: ( 0.00% sparsity) 6
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lin_nnzj: (------% sparsity)
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nln_nnzj: ( 0.00% sparsity) 6
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Counters:
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obj: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0 grad: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0 cons: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0

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