Merge pull request #180 from haampie/remove-randomized-algorithms

Move randomized algorithms from the package

Author: haampie

README.md

@@ -23,6 +23,4 @@ Pkg.add("IterativeSolvers")
 
 - [Issue #1](https://github.com/JuliaMath/IterativeSolvers.jl/issues/1) documents the implementation roadmap for iterative solvers.
 
-- The interfaces between the various algorithms are still in flux, but aim to be consistent.
-
-- [Issue #33](https://github.com/JuliaMath/IterativeSolvers.jl/issues/33) documents the implementation roadmap for randomized algorithms.
+- The interfaces between the various algorithms are still in flux, but aim to be consistent.

docs/make.jl

@@ -25,7 +25,6 @@ makedocs(
 			"Power method" => "eigenproblems/power_method.md",
 		],
 		"SVDL" => "svd/svdl.md",
-		"Randomized algorithms" => "randomized.md",
 		"The iterator approach" => "iterators.md",
 		"About" => [
 			"Contributing" => "about/CONTRIBUTING.md",

docs/src/index.md

@@ -4,7 +4,7 @@ IterativeSolvers.jl is a Julia package that provides efficient iterative algorit
 
 For bug reports, feature requests and questions please submit an issue. If you're interested in contributing, please see the [Contributing](@ref) guide.
 
-For more information on future methods have a look at the package [roadmap](https://github.com/JuliaLang/IterativeSolvers.jl/issues/1) on deterministic methods, for randomized algorithms check [here](https://github.com/JuliaLang/IterativeSolvers.jl/issues/33).
+For more information on future methods have a look at the package [roadmap](https://github.com/JuliaLang/IterativeSolvers.jl/issues/1).
 
 ## What method should I use for linear systems?
 
@@ -29,9 +29,3 @@ When solving **least-squares** problems we currently offer just [LSMR](@ref LSMR
 For the Singular Value Decomposition we offer [SVDL](@ref SVDL), which is the Golub-Kahan-Lanczos procedure.
 
 For eigenvalue problems we have at this point just the [Power Method](@ref PowerMethod) and some convenience wrappers to do shift-and-invert.
-
-## Randomized algorithms
-
-[Randomized algorithms](@ref Randomized) have gotten some traction lately. Some of the methods mentioned in [^Halko2011] have been implemented as well, although their quality is generally poor. Also note that many classical methods such as the subspace iteration, BiCG and recent methods like IDR(s) are also "randomized" in some sense.
-
-[^Halko2011]: Halko, Nathan, Per-Gunnar Martinsson, and Joel A. Tropp. "Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions." SIAM review 53.2 (2011): 217-288.

docs/src/randomized.md

@@ -11,11 +11,10 @@ include("common.jl")
 include("orthogonalize.jl")
 include("history.jl")
 
-#Specialized factorizations
-include("factorization.jl")
+# Factorizations
 include("hessenberg.jl")
 
-#Linear solvers
+# Linear solvers
 include("stationary.jl")
 include("stationary_sparse.jl")
 include("cg.jl")
@@ -25,19 +24,14 @@ include("gmres.jl")
 include("chebyshev.jl")
 include("idrs.jl")
 
-#Eigensolvers
+# Eigensolvers
 include("simple.jl")
 
-#SVD solvers
+# SVD solvers
 include("svdl.jl")
 
-#Least-squares
+# Least-squares
 include("lsqr.jl")
 include("lsmr.jl")
 
-#Randomized algorithms
-include("rlinalg.jl")
-include("rsvd.jl")
-include("rsvd_fnkz.jl")
-
 end