Add iterator interface

docs/src/user/minimization.md

@@ -235,3 +235,23 @@ line search errors if `initial_x` is a stationary point. Notice, that this is on
 a first order check. If `initial_x` is any type of stationary point, `g_converged`
 will be true. This includes local minima, saddle points, and local maxima. If `iterations` is `0`
 and `g_converged` is `true`, the user needs to keep this point in mind.
+
+## Iterator interface
+For multivariable optimizations, iterator interface is provided through `Optim.optimizing`
+function.  Using this interface, `optimize(args...; kwargs...)` is equivalent to
+
+```jl
+let istate
+    iter = Optim.optimizing(args...; kwargs...)
+    for istate′ in iter
+        istate = istate′
+    end
+    Optim.OptimizationResults(iter, istate)
+end
+```
+
+The iterator returned by `Optim.optimizing` yields an iterator state for each iteration
+step.
+
+Functions that can be called on the result object (e.g. `minimizer`, `iterations`; see
+[Complete list of functions](@ref)) can be used on the iteration state `istate`.

src/api.jl

@@ -1,4 +1,7 @@
-Base.summary(r::OptimizationResults) = summary(r.method) # might want to do more here than just return summary of the method used
+_method(r::OptimizationResults) = r.method
+
+Base.summary(r::Union{OptimizationResults, OptimIterator}) =
+    summary(_method(r)) # might want to do more here than just return summary of the method used
 minimizer(r::OptimizationResults) = r.minimizer
 minimum(r::OptimizationResults) = r.minimum
 iterations(r::OptimizationResults) = r.iterations
@@ -35,24 +38,24 @@ end
 x_upper_trace(r::MultivariateOptimizationResults) =
     error("x_upper_trace is not implemented for $(summary(r)).")
 
-function x_trace(r::MultivariateOptimizationResults)
+function x_trace(r::Union{MultivariateOptimizationResults, IteratorState})
     tr = trace(r)
-    if isa(r.method, NelderMead)
+    if isa(_method(r), NelderMead)
         throw(
             ArgumentError(
                 "Nelder Mead does not operate with a single x. Please use either centroid_trace(...) or simplex_trace(...) to extract the relevant points from the trace.",
             ),
         )
     end
     !haskey(tr[1].metadata, "x") && error(
-        "Trace does not contain x. To get a trace of x, run optimize() with extended_trace = true",
+        "Trace does not contain x. To get a trace of x, run optimize() with extended_trace = true and make sure x is stored in the trace for the method of choice.",
     )
     [state.metadata["x"] for state in tr]
 end
 
-function centroid_trace(r::MultivariateOptimizationResults)
+function centroid_trace(r::Union{MultivariateOptimizationResults, IteratorState})
     tr = trace(r)
-    if !isa(r.method, NelderMead)
+    if !isa(_method(r), NelderMead)
         throw(
             ArgumentError(
                 "There is no centroid involved in optimization using $(r.method). Please use x_trace(...) to grab the points from the trace.",
@@ -64,9 +67,9 @@ function centroid_trace(r::MultivariateOptimizationResults)
     )
     [state.metadata["centroid"] for state in tr]
 end
-function simplex_trace(r::MultivariateOptimizationResults)
+function simplex_trace(r::Union{MultivariateOptimizationResults, IteratorState})
     tr = trace(r)
-    if !isa(r.method, NelderMead)
+    if !isa(_method(r), NelderMead)
         throw(
             ArgumentError(
                 "There is no simplex involved in optimization using $(r.method). Please use x_trace(...) to grab the points from the trace.",
@@ -78,9 +81,9 @@ function simplex_trace(r::MultivariateOptimizationResults)
     )
     [state.metadata["simplex"] for state in tr]
 end
-function simplex_value_trace(r::MultivariateOptimizationResults)
+function simplex_value_trace(r::Union{MultivariateOptimizationResults, IteratorState})
     tr = trace(r)
-    if !isa(r.method, NelderMead)
+    if !isa(_method(r), NelderMead)
         throw(
             ArgumentError(
                 "There are no simplex values involved in optimization using $(r.method). Please use f_trace(...) to grab the objective values from the trace.",
@@ -94,10 +97,10 @@ function simplex_value_trace(r::MultivariateOptimizationResults)
 end
 
 
-f_trace(r::OptimizationResults) = [state.value for state in trace(r)]
+f_trace(r::Union{OptimizationResults, IteratorState}) = [state.value for state in trace(r)]
 g_norm_trace(r::OptimizationResults) =
     error("g_norm_trace is not implemented for $(summary(r)).")
-g_norm_trace(r::MultivariateOptimizationResults) = [state.g_norm for state in trace(r)]
+g_norm_trace(r::Union{MultivariateOptimizationResults, IteratorState}) = [state.g_norm for state in trace(r)]
 
 f_calls(r::OptimizationResults) = r.f_calls
 f_calls(d) = first(d.f_calls)
@@ -114,7 +117,7 @@ h_calls(d) = first(d.h_calls)
 h_calls(d::TwiceDifferentiableHV) = first(d.hv_calls)
 
 converged(r::UnivariateOptimizationResults) = r.stopped_by.converged
-function converged(r::MultivariateOptimizationResults)
+function converged(r::Union{MultivariateOptimizationResults, IteratorState})
     conv_flags = r.stopped_by.x_converged || r.stopped_by.f_converged || r.stopped_by.g_converged
     x_isfinite = isfinite(x_abschange(r)) || isnan(x_relchange(r))
     f_isfinite = if r.stopped_by.iterations > 0

src/deprecate.jl

@@ -1,105 +0,0 @@
-Base.@deprecate method(x) summary(x)
-
-const has_deprecated_fminbox = Ref(false)
-function optimize(
-    df::OnceDifferentiable,
-    initial_x::Array{T},
-    l::Array{T},
-    u::Array{T},
-    ::Type{Fminbox};
-    x_tol::T = eps(T),
-    f_tol::T = sqrt(eps(T)),
-    g_tol::T = sqrt(eps(T)),
-    allow_f_increases::Bool = true,
-    iterations::Integer = 1_000,
-    store_trace::Bool = false,
-    show_trace::Bool = false,
-    extended_trace::Bool = false,
-    show_warnings::Bool = true,
-    callback = nothing,
-    show_every::Integer = 1,
-    linesearch = LineSearches.HagerZhang{T}(),
-    eta::Real = convert(T, 0.4),
-    mu0::T = convert(T, NaN),
-    mufactor::T = convert(T, 0.001),
-    precondprep = (P, x, l, u, mu) -> precondprepbox!(P, x, l, u, mu),
-    optimizer = ConjugateGradient,
-    optimizer_o = Options(
-        store_trace = store_trace,
-        show_trace = show_trace,
-        extended_trace = extended_trace,
-        show_warnings = show_warnings,
-    ),
-    nargs...,
-) where {T<:AbstractFloat}
-    if !has_deprecated_fminbox[]
-        @warn(
-            "Fminbox with the optimizer keyword is deprecated, construct Fminbox{optimizer}() and pass it to optimize(...) instead."
-        )
-        has_deprecated_fminbox[] = true
-    end
-    optimize(
-        df,
-        initial_x,
-        l,
-        u,
-        Fminbox{optimizer}();
-        allow_f_increases = allow_f_increases,
-        iterations = iterations,
-        store_trace = store_trace,
-        show_trace = show_trace,
-        extended_trace = extended_trace,
-        show_warnings = show_warnings,
-        show_every = show_every,
-        callback = callback,
-        linesearch = linesearch,
-        eta = eta,
-        mu0 = mu0,
-        mufactor = mufactor,
-        precondprep = precondprep,
-        optimizer_o = optimizer_o,
-    )
-end
-
-function optimize(::AbstractObjective)
-    throw(
-        ErrorException(
-            "Optimizing an objective `obj` without providing an initial `x` has been deprecated without backwards compatability. Please explicitly provide an `x`: `optimize(obj, x)``",
-        ),
-    )
-end
-function optimize(::AbstractObjective, ::Method)
-    throw(
-        ErrorException(
-            "Optimizing an objective `obj` without providing an initial `x` has been deprecated without backwards compatability. Please explicitly provide an `x`: `optimize(obj, x, method)``",
-        ),
-    )
-end
-function optimize(::AbstractObjective, ::Method, ::Options)
-    throw(
-        ErrorException(
-            "Optimizing an objective `obj` without providing an initial `x` has been deprecated without backwards compatability. Please explicitly provide an `x`: `optimize(obj, x, method, options)``",
-        ),
-    )
-end
-function optimize(::AbstractObjective, ::Options)
-    throw(
-        ErrorException(
-            "Optimizing an objective `obj` without providing an initial `x` has been deprecated without backwards compatability. Please explicitly provide an `x`: `optimize(obj, x, options)``",
-        ),
-    )
-end
-
-function optimize(
-    df::OnceDifferentiable,
-    l::Array{T},
-    u::Array{T},
-    F::Fminbox{O};
-    kwargs...,
-) where {T<:AbstractFloat,O<:AbstractOptimizer}
-    throw(
-        ErrorException(
-            "Optimizing an objective `obj` without providing an initial `x` has been deprecated without backwards compatability. Please explicitly provide an `x`: `optimize(obj, x, l, u, method, options)``",
-        ),
-    )
-end

src/multivariate/optimize/interface.jl

@@ -159,7 +159,7 @@
 ) = td
 
 # if no method or options are present
-function optimize(
+function optimizing(
     f,
     initial_x::AbstractArray;
     inplace = true,
@@ -173,9 +173,9 @@
     add_default_opts!(checked_kwargs, method)
 
     options = Options(; checked_kwargs...)
-    optimize(d, initial_x, method, options)
+    optimizing(d, initial_x, method, options)
 end
-function optimize(
+function optimizing(
     f,
     g,
     initial_x::AbstractArray;
@@ -190,9 +190,9 @@
     add_default_opts!(checked_kwargs, method)
 
     options = Options(; checked_kwargs...)
-    optimize(d, initial_x, method, options)
+    optimizing(d, initial_x, method, options)
 end
-function optimize(
+function optimizing(
     f,
     g,
     h,
@@ -208,19 +208,15 @@
     add_default_opts!(checked_kwargs, method)
 
     options = Options(; checked_kwargs...)
-    optimize(d, initial_x, method, options)
+    optimizing(d, initial_x, method, options)
 end
 
 # no method supplied with objective
-function optimize(
-    d::T,
-    initial_x::AbstractArray,
-    options::Options,
-) where {T<:AbstractObjective}
-    optimize(d, initial_x, fallback_method(d), options)
+function optimizing(d::AbstractObjective, initial_x::AbstractArray, options::Options)
+    optimizing(d, initial_x, fallback_method(d), options)
 end
 # no method supplied with inplace and autodiff keywords becauase objective is not supplied
-function optimize(
+function optimizing(
     f,
     initial_x::AbstractArray,
     options::Options;
@@ -229,9 +225,9 @@
 )
     method = fallback_method(f)
     d = promote_objtype(method, initial_x, autodiff, inplace, f)
-    optimize(d, initial_x, method, options)
+    optimizing(d, initial_x, method, options)
 end
-function optimize(
+function optimizing(
     f,
     g,
     initial_x::AbstractArray,
@@ -242,9 +238,9 @@
 
     method = fallback_method(f, g)
     d = promote_objtype(method, initial_x, autodiff, inplace, f, g)
-    optimize(d, initial_x, method, options)
+    optimizing(d, initial_x, method, options)
 end
-function optimize(
+function optimizing(
     f,
     g,
     h,
@@ -257,11 +253,11 @@
     method = fallback_method(f, g, h)
     d = promote_objtype(method, initial_x, autodiff, inplace, f, g, h)
 
-    optimize(d, initial_x, method, options)
+    optimizing(d, initial_x, method, options)
 end
 
 # potentially everything is supplied (besides caches)
-function optimize(
+function optimizing(
     f,
     initial_x::AbstractArray,
     method::AbstractOptimizer,
@@ -271,7 +267,7 @@
 )
 
     d = promote_objtype(method, initial_x, autodiff, inplace, f)
-    optimize(d, initial_x, method, options)
+    optimizing(d, initial_x, method, options)
 end
 function optimize(
     f,
@@ -286,7 +282,7 @@
     d = promote_objtype(method, initial_x, autodiff, inplace, f)
     optimize(d, c, initial_x, method, options)
 end
-function optimize(
+function optimizing(
     f,
     g,
     initial_x::AbstractArray,
@@ -298,9 +294,9 @@
 
     d = promote_objtype(method, initial_x, autodiff, inplace, f, g)
 
-    optimize(d, initial_x, method, options)
+    optimizing(d, initial_x, method, options)
 end
-function optimize(
+function optimizing(
     f,
     g,
     h,
@@ -313,17 +309,29 @@
 
     d = promote_objtype(method, initial_x, autodiff, inplace, f, g, h)
 
-    optimize(d, initial_x, method, options)
+    optimizing(d, initial_x, method, options)
 end
 
-function optimize(
+function optimizing(
     d::D,
     initial_x::AbstractArray,
     method::SecondOrderOptimizer,
     options::Options = Options(; default_options(method)...);
     autodiff = :finite,
     inplace = true,
 ) where {D<:Union{NonDifferentiable,OnceDifferentiable}}
+
     d = promote_objtype(method, initial_x, autodiff, inplace, d)
-    optimize(d, initial_x, method, options)
+    optimizing(d, initial_x, method, options)
+end
+
+function optimize(args...; kwargs...)
+    local istate
+    iter = optimizing(args...; kwargs...)
+    for istate′ in iter
+        istate = istate′
+    end
+    # We can safely assume that `istate` is defined at this point.  That is to say,
+    # `OptimIterator` guarantees that `iterate(::OptimIterator) !== nothing`.
+    return OptimizationResults(iter, istate)
 end