remove _unbounded_check + optimize _null_check

paper/code/test.jl

@@ -1,4 +1,4 @@
-using NLPModels, Stopping
+using LinearAlgebra, NLPModels, Stopping
 
 include("NewtonSolver.jl")
 include("Armijo.jl")

pluto/notebook.jl

@@ -0,0 +1,219 @@
+### A Pluto.jl notebook ###
+# v0.12.6
+
+using Markdown
+using InteractiveUtils
+
+# ╔═╡ 24e89936-2048-11eb-2e22-3771c6cbf492
+begin
+	using LinearAlgebra, NLPModels, Stopping, Test
+end
+
+# ╔═╡ 6919e7e4-2047-11eb-1994-67f541031e50
+md"
+# Stopping
+
+[![Build Status](https://travis-ci.org/vepiteski/Stopping.jl.svg?branch=master)](https://travis-ci.org/vepiteski/Stopping.jl)
+[![Coverage Status](https://coveralls.io/repos/vepiteski/Stopping.jl/badge.svg?branch=master&service=github)](https://coveralls.io/github/vepiteski/Stopping.jl?branch=julia-0.7)
+[![](https://img.shields.io/badge/docs-dev-blue.svg)](https://vepiteski.github.io/Stopping.jl/dev/)
+"
+
+# ╔═╡ bdc19b18-2047-11eb-04a0-f75a546514ce
+md"
+## How to install
+Install and test the Stopping package with the Julia package manager:
+```julia
+pkg> add Stopping
+pkg> test Stopping
+```
+You can access the most up-to-date version of the Stopping package using:
+```julia
+pkg> add https://github.com/vepiteski/Stopping.jl
+pkg> test Stopping
+pkg> status Stopping
+```
+"
+
+# ╔═╡ 7d8b468e-2047-11eb-118e-9f3f78f0cb2d
+md"
+## Purpose
+
+Tools to ease the uniformization of stopping criteria in iterative solvers.
+
+When a solver is called on an optimization model, four outcomes may happen:
+
+1. the approximate solution is obtained, the problem is considered solved
+2. the problem is declared unsolvable (unboundedness, infeasibility ...)
+3. the maximum available resources are not sufficient to compute the solution
+4. some algorithm dependent failure happens
+
+This tool eases the first three items above. It defines a type
+
+    mutable struct GenericStopping <: AbstractStopping
+        problem       :: Any                  # an arbitrary instance of a problem
+        meta          :: AbstractStoppingMeta # contains the used parameters and stopping status
+        current_state :: AbstractState        # Current information on the problem
+        main_stp :: Union{AbstractStopping, Nothing} # Stopping of the main problem, or nothing
+        listofstates :: Union{ListStates, Nothing}   # History of states
+        user_specific_struct :: Any                  # User-specific structure
+
+The [StoppingMeta](https://github.com/vepiteski/Stopping.jl/blob/master/src/Stopping/StoppingMetamod.jl) provides default tolerances, maximum resources, ...  as well as (boolean) information on the result.
+
+## Functions
+
+The tool provides two main functions:
+* `start!(stp :: AbstractStopping)` initializes the time and the tolerance at the starting point and check wether the initial guess is optimal.
+* `stop!(stp :: AbstractStopping)` checks optimality of the current guess as well as failure of the system (unboundedness for instance) and maximum resources (number of evaluations of functions, elapsed time ...)
+
+Stopping uses the informations furnished by the State to evaluate its functions. Communication between the two can be done through the following functions:
+* `update_and_start!(stp :: AbstractStopping; kwargs...)` updates the states with informations furnished as kwargs and then call start!.
+* `update_and_stop!(stp :: AbstractStopping; kwargs...)` updates the states with informations furnished as kwargs and then call stop!.
+* `fill_in!(stp :: AbstractStopping, x :: Iterate)` a function that fill in all the State with all the informations required to correctly evaluate the stopping functions. This can reveal useful, for instance, if the user do not trust the informations furnished by the algorithm in the State.
+* `reinit!(stp :: AbstractStopping)` reinitialize the entries of
+the Stopping to reuse for another call.
+"
+
+# ╔═╡ 8f532b5c-2047-11eb-3ece-b5c66e3c1030
+md"
+## Example
+
+As an example, a naive version of the Newton method is provided [here](https://github.com/vepiteski/Stopping.jl/blob/master/test/examples/newton.jl). First we import the packages:
+"
+
+# ╔═╡ 2b84477c-2048-11eb-00cf-4d8cbd285252
+md"
+We consider a quadratic test function, and create an uncontrained quadratic optimization problem using [NLPModels](https://github.com/JuliaSmoothOptimizers/NLPModels.jl):
+"
+
+# ╔═╡ 31305268-2048-11eb-0145-9dded700c8c7
+begin
+	A = rand(5, 5); Q = A' * A;
+	f(x) = 0.5 * x' * Q * x + sum(x)
+	nlp = ADNLPModel(f,  ones(5))
+
+	nlp.meta.nvar
+end
+
+# ╔═╡ 3afd8c90-2048-11eb-1195-b7a16248f31d
+md"
+We now initialize the *NLPStopping*. First create a State. We use [unconstrained_check](https://github.com/vepiteski/Stopping.jl/blob/master/src/Stopping/nlp_admissible_functions.jl) as an optimality function
+"
+
+# ╔═╡ 41b7df4a-2048-11eb-0823-3f28002e28a8
+begin
+	nlp_at_x = NLPAtX(ones(5))
+	#stop_nlp = NLPStopping(nlp, nlp_at_x, optimality_check = unconstrained_check)
+	#Note that, since we used a default State, an alternative would have been:
+	stop_nlp = NLPStopping(nlp)
+
+	stop_nlp.meta.atol
+end
+
+# ╔═╡ 63dc7522-2048-11eb-2d72-5353bdba39eb
+md"
+Now a basic version of Newton to illustrate how to use Stopping.
+"
+
+# ╔═╡ 675e49aa-2048-11eb-2263-65258ce6508e
+begin
+	function newton(stp :: NLPStopping)
+
+		#Notations
+		pb = stp.pb; state = stp.current_state;
+		#Initialization
+		xt = state.x
+
+		#First, call start! to check optimality and set an initial configuration
+		#(start the time counter, set relative error ...)
+		OK = update_and_start!(stp, x = xt, gx = grad(pb, xt), Hx = hess(pb, xt))
+
+		while !OK
+			#Compute the Newton direction (state.Hx only has the lower triangular)
+			d = (state.Hx + state.Hx' - diagm(0 => diag(state.Hx))) \ (- state.gx)
+			#Update the iterate
+			xt = xt + d
+			#Update the State and call the Stopping with stop!
+			OK = update_and_stop!(stp, x = xt, gx = grad(pb, xt), Hx = hess(pb, xt))
+		end
+
+		return stp
+	end
+end
+
+# ╔═╡ 78f87730-2048-11eb-14e4-0b692bf6e7a8
+md"
+Finally, we can call the algorithm with our Stopping, and consult the Stopping to know what happened.
+"
+
+# ╔═╡ 7be7cb08-2048-11eb-31ea-132cefd60433
+begin
+	newton(stop_nlp)	
+
+	#We can then ask stop_nlp the final status
+	status(stop_nlp, list = true)
+end
+
+# ╔═╡ 8de7e950-2048-11eb-3e1b-b3d350e346c8
+begin
+	#Explore the final values in stop_nlp.current_state
+	@show "Final solution is $(stop_nlp.current_state.x)"
+end
+
+# ╔═╡ 9de11048-2048-11eb-3ffc-7fc8110510e4
+md"
+We reached optimality, and thanks to the Stopping structure this simple looking
+algorithm verified at each step of the algorithm:
+- time limit has been respected;
+- evaluations of the problem are not excessive;
+- the problem is not unbounded (w.r.t. x and f(x));
+- there is no NaN in x, f(x), g(x), H(x);
+- the maximum number of iteration (call to stop!) is limited.
+"
+
+# ╔═╡ ceaf484e-2047-11eb-018c-4b5f3696e597
+md"
+### Your Stopping your way
+
+The GenericStopping (with GenericState) provides a complete structure to handle stopping criteria.
+Then, depending on the problem structure, you can specialize a new Stopping by
+redefining a State and some functions specific to your problem.
+
+We provide some specialization of the GenericStopping for optimization:
+  * [NLPStopping](https://github.com/vepiteski/Stopping.jl/blob/master/src/Stopping/NLPStoppingmod.jl) with [NLPAtX](https://github.com/vepiteski/Stopping.jl/blob/master/src/State/NLPAtXmod.jl) as a specialized State: for non-linear programming (based on [NLPModels](https://github.com/JuliaSmoothOptimizers/NLPModels.jl));
+  * [LAStopping](https://github.com/vepiteski/Stopping.jl/blob/master/src/Stopping/LinearAlgebraStopping.jl) with [GenericState](https://github.com/vepiteski/Stopping.jl/blob/master/src/State/GenericStatemod.jl): for linear algebra problems.
+  * [LS_Stopping](https://github.com/vepiteski/Stopping.jl/blob/master/src/Stopping/LineSearchStoppingmod.jl) with [LSAtT](https://github.com/vepiteski/Stopping.jl/blob/master/src/State/LSAtTmod.jl) as a specialized State: for 1d optimization;
+  * more to come...
+
+### How To Stopping
+
+Consult the [HowTo tutorial](https://github.com/vepiteski/Stopping.jl/blob/master/test/examples/runhowto.jl) to learn more about the possibilities offered by Stopping.
+
+You can also access other examples of algorithms in the [test/examples](https://github.com/vepiteski/Stopping.jl/blob/master/test/examples/) folder, which for instance illustrate the strenght of Stopping with subproblems:
+* Consult the [OptimSolver tutorial](https://github.com/vepiteski/Stopping.jl/blob/master/test/examples/run-optimsolver.jl) for more on how to use Stopping with nested algorithms.
+* Check the [Benchmark tutorial](https://github.com/vepiteski/Stopping.jl/blob/master/test/examples/benchmark.jl) to see how Stopping can combined with [SolverBenchmark.jl](https://juliasmoothoptimizers.github.io/SolverBenchmark.jl/).
+* Stopping can be adapted to closed solvers via a buffer function as in [Buffer tutorial](https://github.com/vepiteski/Stopping.jl/blob/master/test/examples/buffer.jl) for an instance with [Ipopt](https://github.com/JuliaOpt/Ipopt.jl) via [NLPModelsIpopt](https://github.com/JuliaSmoothOptimizers/NLPModelsIpopt.jl).
+
+## Long-Term Goals
+
+Stopping is aimed as a tool for improving the reusability and robustness in the implementation of iterative algorithms. We warmly welcome any feedback or comment leading to potential improvements.
+
+Future work will address more sophisticated problems such as mixed-integer optimization problems, optimization with uncertainty. The list of suggested optimality functions will be enriched with state of the art conditions.
+"
+
+# ╔═╡ Cell order:
+# ╟─6919e7e4-2047-11eb-1994-67f541031e50
+# ╟─bdc19b18-2047-11eb-04a0-f75a546514ce
+# ╟─7d8b468e-2047-11eb-118e-9f3f78f0cb2d
+# ╟─8f532b5c-2047-11eb-3ece-b5c66e3c1030
+# ╠═24e89936-2048-11eb-2e22-3771c6cbf492
+# ╠═2b84477c-2048-11eb-00cf-4d8cbd285252
+# ╠═31305268-2048-11eb-0145-9dded700c8c7
+# ╟─3afd8c90-2048-11eb-1195-b7a16248f31d
+# ╠═41b7df4a-2048-11eb-0823-3f28002e28a8
+# ╟─63dc7522-2048-11eb-2d72-5353bdba39eb
+# ╠═675e49aa-2048-11eb-2263-65258ce6508e
+# ╟─78f87730-2048-11eb-14e4-0b692bf6e7a8
+# ╠═7be7cb08-2048-11eb-31ea-132cefd60433
+# ╠═8de7e950-2048-11eb-3e1b-b3d350e346c8
+# ╟─9de11048-2048-11eb-3ffc-7fc8110510e4
+# ╟─ceaf484e-2047-11eb-018c-4b5f3696e597

src/State/GenericStatemod.jl

@@ -110,38 +110,44 @@ function reinit!(stateatx :: AbstractState; kwargs...)
 end
 
 """
-\\_domain\\_check: returns true if there is a NaN in the State entries, false otherwise
+\\_domain\\_check: returns true if there is a *NaN* or a *Missing* in the state entries (short-circuiting), false otherwise.
 
-`_domain_check(:: AbstractState)`
+`_domain_check(:: AbstractState; kwargs...)`
+
+Note:
+- The fields given as keys in kwargs are not checked.
 
 Examples:
 \\_domain\\_check(state1)
+\\_domain\\_check(state1, x = true)
 """
-function _domain_check(stateatx :: AbstractState)
- domainerror = false
+function _domain_check(stateatx :: T; kwargs...) where T <: AbstractState
 
- for k ∈ fieldnames(typeof(stateatx))
-     _temp = getfield(stateatx, k)
-     domainerror = domainerror || _check_nan(_temp)
+ for k ∈ setdiff(fieldnames(T), keys(kwargs))
+
+     gf = getfield(stateatx, k)
+     if _check_nan_miss(gf)
+         return true
+     end
  end
 
- return domainerror
+ return false
 end
 
-_check_nan(field :: Any) = false #Nothing or Counters
-_check_nan(field :: SparseMatrixCSC) = any(isnan, field.nzval) #because checking in sparse matrices is too slow
-_check_nan(field :: Union{AbstractVector,AbstractMatrix}) = any(isnan, field)
-_check_nan(field :: AbstractFloat) = isnan(field)
+_check_nan_miss(field :: Any) = false #Nothing or Counters
+_check_nan_miss(field :: SparseMatrixCSC) = any(isnan, field.nzval) #because checking in sparse matrices is too slow
+_check_nan_miss(field :: Union{AbstractVector,AbstractMatrix}) = any(isnan, field)
+_check_nan_miss(field :: AbstractFloat) = ismissing(field) || isnan(field)
 
 import Base.copy
 ex=:(_genobj(typ)=$(Expr(:new, :typ))); eval(ex)
-function copy(state :: AbstractState)
+function copy(state :: T) where T <: AbstractState
 
  #ex=:(_genobj(typ)=$(Expr(:new, :typ))); eval(ex)
- cstate = _genobj(typeof(state))
+ cstate = _genobj(T)
  #cstate = $(Expr(:new, typeof(state)))
 
- for k ∈ fieldnames(typeof(state))
+ for k ∈ fieldnames(T)
   setfield!(cstate, k, deepcopy(getfield(state, k)))
  end
 
@@ -162,14 +168,14 @@ corresponding entries are replaced by a vector of size 1 containing its pnorm-no
 
 see also: copy, copy\\_compress\\_state, ListStates
 """
-function compress_state!(stateatx        :: AbstractState;
+function compress_state!(stateatx        :: T;
                          save_matrix     :: Bool    = false,
                          max_vector_size :: Int     = length(stateatx.x),
                          pnorm           :: Float64 = Inf,
                          keep            :: Bool    = false,
-                         kwargs...)
+                         kwargs...)  where T <: AbstractState
 
-  for k ∈ fieldnames(typeof(stateatx))
+  for k ∈ fieldnames(T)
    if k ∈ keys(kwargs) && !keep
     try
         setfield!(stateatx, k, nothing)

src/State/NLPAtXmod.jl

@@ -113,7 +113,7 @@ prioritized over the existing *x*, *lambda* and the default *Counters*.
 """
 function reinit!(stateatx :: NLPAtX, x :: AbstractVector, l :: AbstractVector; kwargs...)
 
- for k ∈ fieldnames(typeof(stateatx))
+ for k ∈ fieldnames(NLPAtX)
    if k ∉ [:x,:lambda,:evals] setfield!(stateatx, k, nothing) end
  end