add osborn2 (#147)

src/ADNLPProblems/osborne2.jl

@@ -0,0 +1,78 @@
+export osborne2
+
+function osborne2(args...; n::Int = default_nvar, type::Val{T} = Val(Float64), kwargs...) where {T}
+  y = T[
+    1.366,
+    1.191,
+    1.112,
+    1.013,
+    0.991,
+    0.885,
+    0.831,
+    0.847,
+    0.786,
+    0.725,
+    0.746,
+    0.679,
+    0.608,
+    0.655,
+    0.616,
+    0.606,
+    0.602,
+    0.626,
+    0.651,
+    0.724,
+    0.649,
+    0.649,
+    0.694,
+    0.644,
+    0.624,
+    0.661,
+    0.612,
+    0.558,
+    0.533,
+    0.495,
+    0.500,
+    0.423,
+    0.395,
+    0.375,
+    0.372,
+    0.391,
+    0.396,
+    0.405,
+    0.428,
+    0.429,
+    0.523,
+    0.562,
+    0.607,
+    0.653,
+    0.672,
+    0.708,
+    0.633,
+    0.668,
+    0.645,
+    0.632,
+    0.591,
+    0.559,
+    0.597,
+    0.625,
+    0.739,
+    0.710,
+    0.729,
+    0.720,
+    0.636,
+    0.581,
+    0.428,
+    0.292,
+    0.162,
+    0.098,
+    0.054,
+  ]
+    m = 65
+
+    function f(x)
+      return  sum((y[i] - (x[1] * exp(-T(i - 1)/10 * x[5])+ x[2] * exp(-(T(i-1)/10 - x[9])^2 * x[6]) + x[3]*exp(-(T(i-1)/10 - x[10])^2*x[7]) + x[4] * exp(-(T(i-1)/10 - x[11])^2 * x[8]) ))^2 for i = 1:m)
+    end
+    x0 = T[1.3, 0.65, 0.65, 0.7, 0.6, 3, 5, 7, 2, 4.5, 5.5]
+    return ADNLPModels.ADNLPModel(f, x0, name = "osborne2"; kwargs...)
+  end 

src/Meta/osborne2.jl

@@ -0,0 +1,25 @@
+osborne2_meta = Dict(
+  :nvar => 11,
+  :variable_nvar => false,
+  :ncon => 0,
+  :variable_ncon => false,
+  :minimize => true,
+  :name => "osborne2",
+  :has_equalities_only => false,
+  :has_inequalities_only => false,
+  :has_bounds => false,
+  :has_fixed_variables => false,
+  :objtype => :other,
+  :contype => :unconstrained,
+  :best_known_lower_bound => -Inf,
+  :best_known_upper_bound => 2.093419514212065,
+  :is_feasible => true,
+  :defined_everywhere => missing,
+  :origin => :unknown,
+)
+get_osborne2_nvar(; n::Integer = default_nvar, kwargs...) = 11
+get_osborne2_ncon(; n::Integer = default_nvar, kwargs...) = 0
+get_osborne2_nlin(; n::Integer = default_nvar, kwargs...) = 0
+get_osborne2_nnln(; n::Integer = default_nvar, kwargs...) = 0
+get_osborne2_nequ(; n::Integer = default_nvar, kwargs...) = 0
+get_osborne2_nineq(; n::Integer = default_nvar, kwargs...) = 0

src/PureJuMP/osborne2.jl

@@ -0,0 +1,89 @@
+#   Source:  Problem 19 in
+#   J.J. More', B.S. Garbow and K.E. Hillstrom,
+#   "Testing Unconstrained Optimization Software",
+#   ACM Transactions on Mathematical Software, vol. 7(1), pp. 17-41, 1981.
+
+#   See also Buckley#32 (p.78).
+
+#   classification NOR2-MN-11-65
+export osborne2
+
+function osborne2(args...; n::Int = default_nvar, kwargs...)
+  y = [
+    1.366,
+    1.191,
+    1.112,
+    1.013,
+    0.991,
+    0.885,
+    0.831,
+    0.847,
+    0.786,
+    0.725,
+    0.746,
+    0.679,
+    0.608,
+    0.655,
+    0.616,
+    0.606,
+    0.602,
+    0.626,
+    0.651,
+    0.724,
+    0.649,
+    0.649,
+    0.694,
+    0.644,
+    0.624,
+    0.661,
+    0.612,
+    0.558,
+    0.533,
+    0.495,
+    0.500,
+    0.423,
+    0.395,
+    0.375,
+    0.372,
+    0.391,
+    0.396,
+    0.405,
+    0.428,
+    0.429,
+    0.523,
+    0.562,
+    0.607,
+    0.653,
+    0.672,
+    0.708,
+    0.633,
+    0.668,
+    0.645,
+    0.632,
+    0.591,
+    0.559,
+    0.597,
+    0.625,
+    0.739,
+    0.710,
+    0.729,
+    0.720,
+    0.636,
+    0.581,
+    0.428,
+    0.292,
+    0.162,
+    0.098,
+    0.054,
+  ]
+    m = 65
+
+    nlp = Model()
+    x0 = [1.3, 0.65, 0.65, 0.7, 0.6, 3, 5, 7, 2, 4.5, 5.5]
+    @variable(nlp, x[i=1:11], start=x0[i])
+    @NLobjective(
+    nlp,
+    Min,
+    sum((y[i] - (x[1] * exp(-(i - 1)/10 * x[5])+ x[2] * exp(-((i-1)/10 - x[9])^2 * x[6]) + x[3]*exp(-((i-1)/10 - x[10])^2*x[7]) + x[4] * exp(-((i-1)/10 - x[11])^2 * x[8]) ))^2 for i = 1:m))
+    return nlp
+end