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using Mosek | |
using SCS | |
import DSP: conv | |
using Convex | |
conv(x::AbstractVector, y::AbstractVector) = DSP.conv(x,y) | |
conv(x::Variable, y::AbstractVector) = Convex.conv(x,y) | |
conv(x::AbstractVector, y::Variable) = Convex.conv(x,y) | |
using Random | |
using SparseArrays | |
Random.seed!(123) | |
println(" \n \n \n solving Convex \n \n \n") | |
n,m = 100, 10 | |
h = randn(n) | |
x = Vector(sprandn(m, 0.05)) | |
y = conv(h,x)+randn(n+m-1) | |
lambda = 0.01 | |
x0 = Variable(m) | |
# slv = Mosek.Optimizer() | |
slv = SCS.Optimizer(verbose=0) | |
problem = minimize(0.5*square(norm(conv(h,x0)-y))+lambda*norm(x0,1)) | |
solve!(problem, slv) | |
@show x0.value | |
xConvex = x0.value | |
println(" \n \n \n solving cvxpy \n \n \n") | |
using PyCall | |
# Conda.add("cvxpy"; channel="conda-forge") | |
@pyimport cvxpy as cvx | |
slv = cvx.SCS | |
x0 = cvx.Variable(m) | |
yy = reshape(y, (length(y), 1)) | |
problem = cvx.Problem(cvx.Minimize(cvx.sum_squares(cvx.conv(h,x0)-yy)*0.5+cvx.norm1(x0)*lambda)) | |
problem.solve(solver = slv, verbose = true) | |
xcvxpy = x0.value | |
norm(xcvxpy-xConvex) |
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Note: this was updated for Convex 0.13 in the second revision, so go to the first revision for the Convex 0.12 version. Also, uncomment the
Conda.add
command (and dousing Conda
) to installcvxpy
if it's not already installed.