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Update to PyCall >= 1.90.0 #6

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Apr 12, 2019
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Update to PyCall >= 1.90.0 #6

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2 changes: 1 addition & 1 deletion REQUIRE
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Original file line number Diff line number Diff line change
@@ -1,2 +1,2 @@
julia 0.7
PyCall 1.18
PyCall 1.90.0
125 changes: 11 additions & 114 deletions src/CVXOPT.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -36,13 +36,7 @@ function conelp(c,G,h,dims;A=[],b=[],options=Dict())
py_opts = PyObject(options);

# Call cvxopt.solvers.conelp()
sol = solvers[:conelp](cp,Gp,hp,py_dims,A=Ap,b=bp,options=py_opts);

# Convert solution to Julia arrays
sol["x"] = cvxopt_to_julia(sol["x"])
sol["s"] = cvxopt_to_julia(sol["s"])
sol["z"] = cvxopt_to_julia(sol["z"])
sol["y"] = cvxopt_to_julia(sol["y"])
sol = solvers.conelp(cp,Gp,hp,py_dims,A=Ap,b=bp,options=py_opts);

return sol;
end
Expand Down Expand Up @@ -70,21 +64,7 @@ function coneqp(P,q,G,h,dims;A=[],b=[],options=Dict())
py_opts = PyObject(options);

# Call cvxopt.solvers.coneqp()
sol = solvers[:coneqp](Pp,qp,Gp,hp,py_dims,A=Ap,b=bp,options=py_opts);

# Convert solution to Julia arrays
if sol["status"] == "optimal"
sol["s"] = cvxopt_to_julia(sol["s"])
sol["x"] = cvxopt_to_julia(sol["x"])
sol["z"] = cvxopt_to_julia(sol["z"])
sol["y"] = cvxopt_to_julia(sol["y"])
elseif sol["status"] == "dual infeasible"
sol["s"] = cvxopt_to_julia(sol["s"])
sol["x"] = cvxopt_to_julia(sol["x"])
elseif sol["status"] == "primal infeasible"
sol["z"] = cvxopt_to_julia(sol["z"])
sol["y"] = cvxopt_to_julia(sol["y"])
end
sol = solvers.coneqp(Pp,qp,Gp,hp,py_dims,A=Ap,b=bp,options=py_opts);

return sol;
end
Expand All @@ -110,21 +90,7 @@ function lp(c,G,h;A=[],b=[],options=Dict())
py_opts = PyObject(options);

# Call cvxopt.solvers.lp()
sol = solvers[:lp](cp,Gp,hp;A=Ap,b=bp,options=py_opts);

# Convert solution to Julia arrays
if sol["status"] == "optimal"
sol["s"] = cvxopt_to_julia(sol["s"])
sol["x"] = cvxopt_to_julia(sol["x"])
sol["z"] = cvxopt_to_julia(sol["z"])
sol["y"] = cvxopt_to_julia(sol["y"])
elseif sol["status"] == "dual infeasible"
sol["s"] = cvxopt_to_julia(sol["s"])
sol["x"] = cvxopt_to_julia(sol["x"])
elseif sol["status"] == "primal infeasible"
sol["z"] = cvxopt_to_julia(sol["z"])
sol["y"] = cvxopt_to_julia(sol["y"])
end
sol = solvers.lp(cp,Gp,hp;A=Ap,b=bp,options=py_opts);

return sol;
end
Expand All @@ -151,21 +117,7 @@ function qp(P,q,G,h;A=[],b=[],options=Dict())
py_opts = PyObject(options);

# Call cvxopt.solvers.qp()
sol = solvers[:qp](Pp,qp,Gp,hp,A=Ap,b=bp,options=py_opts);

# Convert solution to Julia arrays
if sol["status"] == "optimal"
sol["s"] = cvxopt_to_julia(sol["s"])
sol["x"] = cvxopt_to_julia(sol["x"])
sol["z"] = cvxopt_to_julia(sol["z"])
sol["y"] = cvxopt_to_julia(sol["y"])
elseif sol["status"] == "dual infeasible"
sol["s"] = cvxopt_to_julia(sol["s"])
sol["x"] = cvxopt_to_julia(sol["x"])
elseif sol["status"] == "primal infeasible"
sol["z"] = cvxopt_to_julia(sol["z"])
sol["y"] = cvxopt_to_julia(sol["y"])
end
sol = solvers.qp(Pp,qp,Gp,hp,A=Ap,b=bp,options=py_opts);

return sol;
end
Expand Down Expand Up @@ -200,31 +152,7 @@ function socp(c,Gl,hl,Gq,hq;A=[],b=[],options=Dict())
py_opts = PyObject(options);

# Call cvxopt.solvers.socp()
sol = solvers[:socp](cp, Gl=Glp, hl=hlp, Gq=Gqp, hq=hqp, A=Ap, b=bp, options=py_opts);

# Convert solution to Julia arrays
if sol["status"] == "optimal"
sol["x"] = cvxopt_to_julia(sol["x"])
sol["y"] = cvxopt_to_julia(sol["y"])
sol["sl"] = cvxopt_to_julia(sol["sl"]);
sol["zl"] = cvxopt_to_julia(sol["zl"]);
for i = 1:length(Gq)
sol["sq"][i] = cvxopt_to_julia(sol["sq"][i]);
sol["zq"][i] = cvxopt_to_julia(sol["zq"][i]);
end
elseif sol["status"] == "dual infeasible"
sol["sl"] = cvxopt_to_julia(sol["sl"])
sol["x"] = cvxopt_to_julia(sol["x"])
for i = 1:length(Gq)
sol["sq"][i] = cvxopt_to_julia(sol["sq"][i]);
end
elseif sol["status"] == "primal infeasible"
sol["zl"] = cvxopt_to_julia(sol["zl"])
sol["y"] = cvxopt_to_julia(sol["y"])
for i = 1:length(Gq)
sol["zq"][i] = cvxopt_to_julia(sol["zq"][i]);
end
end
sol = solvers.socp(cp, Gl=Glp, hl=hlp, Gq=Gqp, hq=hqp, A=Ap, b=bp, options=py_opts);

return sol;
end
Expand Down Expand Up @@ -259,31 +187,7 @@ function sdp(c, Gl, hl, Gs, hs; A=[], b=[], options=Dict())
py_opts = PyObject(options);

# Call cvxopt.solvers.sdp()
sol = solvers[:sdp](cp, Gl=Glp, hl=hlp, Gs=Gsp, hs=hsp, A=Ap, b=bp, options=py_opts);

# Convert solution to Julia arrays
if sol["status"] == "optimal"
sol["x"] = cvxopt_to_julia(sol["x"])
sol["y"] = cvxopt_to_julia(sol["y"])
sol["sl"] = cvxopt_to_julia(sol["sl"]);
sol["zl"] = cvxopt_to_julia(sol["zl"]);
for i = 1:length(Gs)
sol["ss"][i] = cvxopt_to_julia(sol["ss"][i]);
sol["zs"][i] = cvxopt_to_julia(sol["zs"][i]);
end
elseif sol["status"] == "dual infeasible"
sol["sl"] = cvxopt_to_julia(sol["sl"])
sol["x"] = cvxopt_to_julia(sol["x"])
for i = 1:length(Gs)
sol["ss"][i] = cvxopt_to_julia(sol["ss"][i]);
end
elseif sol["status"] == "primal infeasible"
sol["zl"] = cvxopt_to_julia(sol["zl"])
sol["y"] = cvxopt_to_julia(sol["y"])
for i = 1:length(Gs)
sol["zs"][i] = cvxopt_to_julia(sol["zs"][i]);
end
end
sol = solvers.sdp(cp, Gl=Glp, hl=hlp, Gs=Gsp, hs=hsp, A=Ap, b=bp, options=py_opts);

return sol
end
Expand All @@ -299,28 +203,21 @@ function julia_to_cvxopt(A)
if issparse(A)
J = zeros(Int64, length(A.rowval));
for i = 1:size(A,2)
J[A.colptr[i]:A.colptr[i+1]-1] .= i;
J[A.colptr[i]:A.colptr[i+1]-1] .= i - 1;
end
Ap = cvxopt[:spmatrix](PyVector(A.nzval),PyVector(A.rowval.-1),PyVector(J.-1),(size(A,1),size(A,2)));
I = A.rowval .- 1
V = A.nzval
Ap = @pycall cvxopt.spmatrix(py"list($$V)"o,py"list($$I)"o,py"list($$J)"o,(size(A,1),size(A,2)))::PyObject;
elseif isempty(A)
Ap = pybuiltin("None");
else
sA = size(A)
if length(sA) == 1
sA = (sA[1],1)
end
Ap = cvxopt[:matrix](A[:],sA);
Ap = @pycall cvxopt.matrix(py"list($(A[:]))"o,sA)::PyObject;
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You can use PyCall.array2py(vec(A)) to convert to a flattened Python list without making multiple copies. I should probably export a pylist function to do this.

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Thanks for pointing this out—I was looking for something like that, but I didn't find it in the PyCall README.

end
return Ap;
end

"""
Convert CVXOPT matrix to Julia array
"""
function cvxopt_to_julia(A)
m,n = py"$(A).size";
Aj = Array{Float64}(reshape(py"list($(A)[:])",m,n));
return Aj;
end

end