def get_grads(nBatch=1, nz=10, neq=1, nineq=3, Qscale=1.,
Gscale=1., hscale=1., Ascale=1., bscale=1.):
assert(nBatch == 1)
npr.seed(1)
L = np.random.randn(nz, nz)
Q = Qscale * L.dot(L.T)
G = Gscale * npr.randn(nineq, nz)
# h = hscale*npr.randn(nineq)
z0 = npr.randn(nz)
s0 = npr.rand(nineq)
h = G.dot(z0) + s0
A = Ascale * npr.randn(neq, nz)
# b = bscale*npr.randn(neq)
b = A.dot(z0)
p = npr.randn(nBatch, nz)
# print(np.linalg.norm(p))
truez = npr.randn(nBatch, nz)
Q, p, G, h, A, b, truez = [x.astype(np.float64) for x in
[Q, p, G, h, A, b, truez]]
_, zhat, nu, lam, slacks = qp_cvxpy.forward_single_np(Q, p[0], G, h, A, b)
grads = get_grads_torch(Q, p, G, h, A, b, truez)
return [p[0], Q, G, h, A, b, truez], grads
def f(b):
_, zhat, nu, lam, slacks = qp_cvxpy.forward_single_np(Q, p, G, h, A, b)
return 0.5 * np.sum(np.square(zhat - truez))
def f(b):
zhat, nu, lam = qp_cvxpy.forward_single_np(p, Q, G, h, A, b)
return 0.5 * np.sum(np.square(zhat - truez))
def f(A):
A = A.reshape(neq, nz)
_, zhat, nu, lam, slacks = qp_cvxpy.forward_single_np(Q, p, G, h, A, b)
return 0.5 * np.sum(np.square(zhat - truez))
def f(A):
A = A.reshape(neq, nz)
zhat, nu, lam = qp_cvxpy.forward_single_np(p, Q, G, h, A, b)
return 0.5 * np.sum(np.square(zhat - truez))
