lkapelevich

write(
"test2.jl", 
"""
using LinearAlgebra, Random
a = rand(10, 10)
b = Symmetric(a * a')
f = cholesky(b)
"""
)

lkapelevich

using JLD
using SparseArrays
a = sprand(100, 100, 0.1)
JLD.save("a.jld", "a", a)

write(
    "test.jl",
    """
    using JLD
    using SparseArrays

lkapelevich

bd = BenchmarkData(Xdata = Xdata(MvNormal, NoCorrelation), 
		  wdist = BinChoice(),
	             noisedist = Normal(),
		                 SNR = 20.0,
	             nfeatures = 1000,
	              sparsity = 10,
                  nrange = collect(100:20:500)
		            )
m = RelaxDualSubgradient(gamma = 1.0, ConstantStepping(1e-3))
results = benchmark(bd, m)