Define Base.summary with IO argument (#1193)

Author: devmotion

src/api.jl

@@ -1,4 +1,4 @@
-Base.summary(r::OptimizationResults) = summary(r.method) # might want to do more here than just return summary of the method used
+Base.summary(io::IO, r::OptimizationResults) = summary(io, r.method) # 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

src/maximize.jl

@@ -68,7 +68,7 @@ maximizer(r::MaximizationWrapper) = minimizer(res(r))
 maximum(r::Union{UnivariateOptimizationResults,MultivariateOptimizationResults}) =
     throw(MethodError())
 maximum(r::MaximizationWrapper) = -minimum(res(r))
-Base.summary(r::MaximizationWrapper) = summary(res(r))
+Base.summary(io::IO, r::MaximizationWrapper) = summary(io, res(r))
 
 for api_method in (
     :lower_bound,
@@ -101,8 +101,10 @@ for api_method in (
 end
 
 function Base.show(io::IO, r::MaximizationWrapper{<:UnivariateOptimizationResults})
-    @printf io "Results of Maximization Algorithm\n"
-    @printf io " * Algorithm: %s\n" summary(r)
+    println(io, "Results of Maximization Algorithm")
+    print(io, " * Algorithm: ")
+    summary(io, r)
+    println(io)
     @printf io " * Search Interval: [%f, %f]\n" lower_bound(r) upper_bound(r)
     @printf io " * Maximizer: %e\n" maximizer(r)
     @printf io " * Maximum: %e\n" maximum(r)
@@ -117,8 +119,11 @@ end
 function Base.show(io::IO, r::MaximizationWrapper{<:MultivariateOptimizationResults})
     take = Iterators.take
 
-    @printf io "Results of Optimization Algorithm\n"
-    @printf io " * Algorithm: %s\n" summary(r.res)
+    println(io, "Results of Optimization Algorithm")
+    print(io, " * Algorithm: ")
+    summary(io, r.res)
+    println(io)
+
     if length(join(initial_state(r), ",")) < 40
         @printf io " * Starting Point: [%s]\n" join(initial_state(r), ",")
     else

src/multivariate/solvers/constrained/fminbox.jl

@@ -232,7 +232,11 @@ function Fminbox(
     Fminbox(method, promote(mu0, mufactor)..., precondprep) # default optimizer
 end
 
-Base.summary(F::Fminbox) = "Fminbox with $(summary(F.method))"
+function Base.summary(io::IO, F::Fminbox)
+    print(io, "Fminbox with ")
+    summary(io, F.method)
+    return
+end
 
 # barrier_method() constructs an optimizer to solve the barrier problem using m = Fminbox.method as the reference.
 # Essentially it only updates the P and precondprep fields of `m`.

src/multivariate/solvers/constrained/ipnewton/ipnewton.jl

@@ -5,7 +5,7 @@ struct IPNewton{F,Tμ<:Union{Symbol,Number}} <: IPOptimizer{F}
     # TODO: μ0, and show_linesearch were originally in options
 end
 
-Base.summary(::IPNewton) = "Interior Point Newton"
+Base.summary(io::IO, ::IPNewton) = print(io, "Interior Point Newton")
 
 promote_objtype(method::IPNewton, x, autodiff, inplace::Bool, f::TwiceDifferentiable) = f
 promote_objtype(method::IPNewton, x, autodiff, inplace::Bool, f) =

src/multivariate/solvers/constrained/samin.jl

@@ -56,7 +56,7 @@ end
 #     * 2 = summary every temperature change, without param values
 #     * 3 = summary every temperature change, with param values
 #         covered by the trial values. 1: start decreasing temperature immediately
-Base.summary(::SAMIN) = "SAMIN"
+Base.summary(io::IO, ::SAMIN) = print(io, "SAMIN")
 
 function optimize(
     obj_fn,

src/multivariate/solvers/first_order/accelerated_gradient_descent.jl

@@ -12,7 +12,7 @@ struct AcceleratedGradientDescent{IL,L} <: FirstOrderOptimizer
     manifold::Manifold
 end
 
-Base.summary(::AcceleratedGradientDescent) = "Accelerated Gradient Descent"
+Base.summary(io::IO, ::AcceleratedGradientDescent) = print(io, "Accelerated Gradient Descent")
 
 function AcceleratedGradientDescent(;
     alphaguess = LineSearches.InitialPrevious(), # TODO: investigate good defaults

src/multivariate/solvers/first_order/adam.jl

@@ -40,7 +40,7 @@ end
 # could use epsilon = T->sqrt(eps(T)) and input the promoted type
 Adam(; alpha = 0.0001, beta_mean = 0.9, beta_var = 0.999, epsilon = 1e-8) =
     Adam(alpha, beta_mean, beta_var, epsilon, Flat())
-Base.summary(::Adam) = "Adam"
+Base.summary(io::IO, ::Adam) = print(io, "Adam")
 function default_options(method::Adam)
     (; allow_f_increases = true, iterations = 10_000)
 end

src/multivariate/solvers/first_order/adamax.jl

@@ -39,7 +39,7 @@ struct AdaMax{Tα,T,Tm} <: FirstOrderOptimizer
 end
 AdaMax(; alpha = 0.002, beta_mean = 0.9, beta_var = 0.999, epsilon = sqrt(eps(Float64))) =
     AdaMax(alpha, beta_mean, beta_var, epsilon, Flat())
-Base.summary(::AdaMax) = "AdaMax"
+Base.summary(io::IO, ::AdaMax) = print(io, "AdaMax")
 function default_options(method::AdaMax)
     (; allow_f_increases = true, iterations = 10_000)
 end

src/multivariate/solvers/first_order/bfgs.jl

@@ -10,7 +10,7 @@ struct BFGS{IL,L,H,T,TM} <: FirstOrderOptimizer
     manifold::TM
 end
 
-Base.summary(::BFGS) = "BFGS"
+Base.summary(io::IO, ::BFGS) = print(io, "BFGS")
 
 """
 # BFGS

src/multivariate/solvers/first_order/cg.jl

@@ -51,7 +51,7 @@ struct ConjugateGradient{Tf,T,Tprep,IL,L} <: FirstOrderOptimizer
     manifold::Manifold
 end
 
-Base.summary(::ConjugateGradient) = "Conjugate Gradient"
+Base.summary(io::IO, ::ConjugateGradient) = print(io, "Conjugate Gradient")
 
 """
 # Conjugate Gradient Descent