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bnavigator opened this issue Aug 20, 2022 · 10 comments · Fixed by #814
Closed

Passivity test is ill-conditioned #761

bnavigator opened this issue Aug 20, 2022 · 10 comments · Fixed by #814

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@bnavigator
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bnavigator commented Aug 20, 2022 •
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Whack-a-mole!

Now this cvxopt test is flaky passivity_test.py::test_ispassive_edge_cases :

https://github.com/python-control/python-control/runs/7930805491?check_suite_focus=true#step:6:3450

2022-08-20T10:05:12.4606678Z =================================== FAILURES ===================================
2022-08-20T10:05:12.4607612Z _________________ test_ispassive_edge_cases[test_input2-True] __________________
2022-08-20T10:05:12.4607862Z 
2022-08-20T10:05:12.4608255Z test_input = (array([[-3.e+12,  0.e+00],
2022-08-20T10:05:12.4608627Z        [ 0.e+00, -2.e+12]]), array([[0],
2022-08-20T10:05:12.4609085Z        [1]]), array([[-1,  2]]), array([[0.]]))
2022-08-20T10:05:12.4609312Z expected = True
2022-08-20T10:05:12.4609448Z 
2022-08-20T10:05:12.4609567Z     @pytest.mark.parametrize(
2022-08-20T10:05:12.4609948Z         "test_input,expected",
2022-08-20T10:05:12.4610188Z         [((A, B, C, D*0.0), True),
2022-08-20T10:05:12.4610415Z          ((A_d, B, C, D), True),
2022-08-20T10:05:12.4610640Z          ((A*1e12, B, C, D*0), True),
2022-08-20T10:05:12.4610854Z          ((A, B*0, C*0, D), True),
2022-08-20T10:05:12.4611073Z          ((A*0, B, C, D), True),
2022-08-20T10:05:12.4611293Z          ((A*0, B*0, C*0, D*0), True)])
2022-08-20T10:05:12.4611683Z     def test_ispassive_edge_cases(test_input, expected):
2022-08-20T10:05:12.4611958Z         A = test_input[0]
2022-08-20T10:05:12.4612178Z         B = test_input[1]
2022-08-20T10:05:12.4612610Z         C = test_input[2]
2022-08-20T10:05:12.4612970Z         D = test_input[3]
2022-08-20T10:05:12.4613469Z         sys = ss(A, B, C, D)
2022-08-20T10:05:12.4613753Z >       assert(passivity.ispassive(sys) == expected)
2022-08-20T10:05:12.4613952Z 
2022-08-20T10:05:12.4614077Z control/tests/passivity_test.py:115: 
2022-08-20T10:05:12.4614373Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
2022-08-20T10:05:12.4614742Z control/passivity.py:292: in ispassive
2022-08-20T10:05:12.4615096Z     return solve_passivity_LMI(sys, rho=ofp_index, nu=ifp_index) is not None
2022-08-20T10:05:12.4615456Z control/passivity.py:177: in solve_passivity_LMI
2022-08-20T10:05:12.4615750Z     sol = cvx.solvers.sdp(c, Gs=Gs, hs=hs)
2022-08-20T10:05:12.4616316Z /usr/share/miniconda3/envs/test-env/lib/python3.10/site-packages/cvxopt/coneprog.py:4126: in sdp
2022-08-20T10:05:12.4616810Z     sol = conelp(c, G, h, dims, A = A, b = b, primalstart = ps, dualstart = ds, kktsolver = kktsolver, options = options)
2022-08-20T10:05:12.4617643Z /usr/share/miniconda3/envs/test-env/lib/python3.10/site-packages/cvxopt/coneprog.py:1395: in conelp
2022-08-20T10:05:12.4618038Z     misc.update_scaling(W, lmbda, ds, dz)
2022-08-20T10:05:12.4618322Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
2022-08-20T10:05:12.4618485Z 
2022-08-20T10:05:12.4618810Z W = {'beta': [], 'd': <0x1 matrix, tc='d'>, 'di': <0x1 matrix, tc='d'>, 'dnl': <0x1 matrix, tc='d'>, ...}
2022-08-20T10:05:12.4619335Z lmbda = <6x1 matrix, tc='d'>, s = <13x1 matrix, tc='d'>
2022-08-20T10:05:12.4619636Z z = <13x1 matrix, tc='d'>
2022-08-20T10:05:12.4619773Z 
2022-08-20T10:05:12.4619885Z     def update_scaling(W, lmbda, s, z):
2022-08-20T10:05:12.4620221Z         """
2022-08-20T10:05:12.4620607Z         Updates the Nesterov-Todd scaling matrix W and the scaled variable
2022-08-20T10:05:12.4621042Z         lmbda so that on exit
2022-08-20T10:05:12.4621247Z     
2022-08-20T10:05:12.4621547Z               W * zt = W^{-T} * st = lmbda.
2022-08-20T10:05:12.4621773Z     
2022-08-20T10:05:12.4622145Z         On entry, the nonlinear, 'l' and 'q' components of the arguments s
2022-08-20T10:05:12.4622629Z         and z contain W^{-T}*st and W*zt, i.e, the new iterates in the current
2022-08-20T10:05:12.4622920Z         scaling.
2022-08-20T10:05:12.4623119Z     
2022-08-20T10:05:12.4623489Z         The 's' components contain the factors Ls, Lz in a factorization of
2022-08-20T10:05:12.4624030Z         the new iterates in the current scaling, W^{-T}*st = Ls*Ls',
2022-08-20T10:05:12.4624341Z         W*zt = Lz*Lz'.
2022-08-20T10:05:12.4624645Z         """
2022-08-20T10:05:12.4624832Z     
2022-08-20T10:05:12.4625008Z     
2022-08-20T10:05:12.4625390Z         # Nonlinear and 'l' blocks
2022-08-20T10:05:12.4625606Z         #
2022-08-20T10:05:12.4626008Z         #    d :=  d .* sqrt( s ./ z )
2022-08-20T10:05:12.4626266Z         #    lmbda := lmbda .* sqrt(s) .* sqrt(z)
2022-08-20T10:05:12.4626501Z     
2022-08-20T10:05:12.4626752Z         if 'dnl' in W:
2022-08-20T10:05:12.4627026Z             mnl = len(W['dnl'])
2022-08-20T10:05:12.4627256Z         else:
2022-08-20T10:05:12.4627460Z             mnl = 0
2022-08-20T10:05:12.4627713Z         ml = len(W['d'])
2022-08-20T10:05:12.4627934Z         m = mnl + ml
2022-08-20T10:05:12.4628165Z         s[:m] = base.sqrt( s[:m] )
2022-08-20T10:05:12.4628397Z         z[:m] = base.sqrt( z[:m] )
2022-08-20T10:05:12.4629372Z     
2022-08-20T10:05:12.4629630Z         # d := d .* s .* z
2022-08-20T10:05:12.4629940Z         if 'dnl' in W:
2022-08-20T10:05:12.4630299Z             blas.tbmv(s, W['dnl'], n = mnl, k = 0, ldA = 1)
2022-08-20T10:05:12.4630693Z             blas.tbsv(z, W['dnl'], n = mnl, k = 0, ldA = 1)
2022-08-20T10:05:12.4631031Z             W['dnli'][:] = W['dnl'][:] ** -1
2022-08-20T10:05:12.4631420Z         blas.tbmv(s, W['d'], n = ml, k = 0, ldA = 1, offsetA = mnl)
2022-08-20T10:05:12.4631840Z         blas.tbsv(z, W['d'], n = ml, k = 0, ldA = 1, offsetA = mnl)
2022-08-20T10:05:12.4632171Z         W['di'][:] = W['d'][:] ** -1
2022-08-20T10:05:12.4632391Z     
2022-08-20T10:05:12.4632808Z         # lmbda := s .* z
2022-08-20T10:05:12.4633059Z         blas.copy(s, lmbda, n = m)
2022-08-20T10:05:12.4633323Z         blas.tbmv(z, lmbda, n = m, k = 0, ldA = 1)
2022-08-20T10:05:12.4633622Z     
2022-08-20T10:05:12.4633806Z     
2022-08-20T10:05:12.4634057Z         # 'q' blocks.
2022-08-20T10:05:12.4634267Z         #
2022-08-20T10:05:12.4634539Z         # Let st and zt be the new variables in the old scaling:
2022-08-20T10:05:12.4634792Z         #
2022-08-20T10:05:12.4635010Z         #     st = s_k,   zt = z_k
2022-08-20T10:05:12.4635228Z         #
2022-08-20T10:05:12.4635560Z         # and a = sqrt(st' * J * st),  b = sqrt(zt' * J * zt).
2022-08-20T10:05:12.4635811Z         #
2022-08-20T10:05:12.4636209Z         # 1. Compute the hyperbolic Householder transformation 2*q*q' - J
2022-08-20T10:05:12.4636519Z         #    that maps st/a to zt/b.
2022-08-20T10:05:12.4636747Z         #
2022-08-20T10:05:12.4637058Z         #        c = sqrt( (1 + st'*zt/(a*b)) / 2 )
2022-08-20T10:05:12.4637420Z         #        q = (st/a + J*zt/b) / (2*c).
2022-08-20T10:05:12.4637667Z         #
2022-08-20T10:05:12.4637894Z         #    The new scaling point is
2022-08-20T10:05:12.4638110Z         #
2022-08-20T10:05:12.4638578Z         #        wk := betak * sqrt(a/b) * (2*v[k]*v[k]' - J) * q
2022-08-20T10:05:12.4638818Z         #
2022-08-20T10:05:12.4639082Z         #    with betak = W['beta'][k].
2022-08-20T10:05:12.4639302Z         #
2022-08-20T10:05:12.4639636Z         # 3. The scaled variable:
2022-08-20T10:05:12.4639831Z         #
2022-08-20T10:05:12.4640044Z         #        lambda_k0 = sqrt(a*b) * c
2022-08-20T10:05:12.4640423Z         #        lambda_k1 = sqrt(a*b) * ( (2vk*vk' - J) * (-d*q + u/2) )_1
2022-08-20T10:05:12.4640655Z         #
2022-08-20T10:05:12.4640839Z         #    where
2022-08-20T10:05:12.4641026Z         #
2022-08-20T10:05:12.4641267Z         #        u = st/a - J*zt/b
2022-08-20T10:05:12.4641620Z         #        d = ( vk0 * (vk'*u) + u0/2 ) / (2*vk0 *(vk'*q) - q0 + 1).
2022-08-20T10:05:12.4641864Z         #
2022-08-20T10:05:12.4642182Z         # 4. Update scaling
2022-08-20T10:05:12.4642379Z         #
2022-08-20T10:05:12.4642577Z         #        v[k] := wk^1/2
2022-08-20T10:05:12.4642824Z         #              = 1 / sqrt(2*(wk0 + 1)) * (wk + e).
2022-08-20T10:05:12.4643060Z         #        beta[k] *=  sqrt(a/b)
2022-08-20T10:05:12.4643271Z     
2022-08-20T10:05:12.4643455Z         ind = m
2022-08-20T10:05:12.4643731Z         for k in range(len(W['v'])):
2022-08-20T10:05:12.4643952Z     
2022-08-20T10:05:12.4644314Z             v = W['v'][k]
2022-08-20T10:05:12.4644524Z             m = len(v)
2022-08-20T10:05:12.4644728Z     
2022-08-20T10:05:12.4645040Z             # ln = sqrt( lambda_k' * J * lambda_k )
2022-08-20T10:05:12.4645428Z             ln = jnrm2(lmbda, n = m, offset = ind)
2022-08-20T10:05:12.4645658Z     
2022-08-20T10:05:12.4645978Z             # a = sqrt( sk' * J * sk ) = sqrt( st' * J * st )
2022-08-20T10:05:12.4646340Z             # s := s / a = st / a
2022-08-20T10:05:12.4646607Z             aa = jnrm2(s, offset = ind, n = m)
2022-08-20T10:05:12.4646898Z             blas.scal(1.0/aa, s, offset = ind, n = m)
2022-08-20T10:05:12.4647123Z     
2022-08-20T10:05:12.4647455Z             # b = sqrt( zk' * J * zk ) = sqrt( zt' * J * zt )
2022-08-20T10:05:12.4647818Z             # z := z / a = zt / b
2022-08-20T10:05:12.4648054Z             bb = jnrm2(z, offset = ind, n = m)
2022-08-20T10:05:12.4648334Z             blas.scal(1.0/bb, z, offset = ind, n = m)
2022-08-20T10:05:12.4648565Z     
2022-08-20T10:05:12.4648857Z             # c = sqrt( ( 1 + (st'*zt) / (a*b) ) / 2 )
2022-08-20T10:05:12.4649290Z             cc = math.sqrt( ( 1.0 + blas.dot(s, z, offsetx = ind, offsety =
2022-08-20T10:05:12.4649582Z                 ind, n = m) ) / 2.0 )
2022-08-20T10:05:12.4649796Z     
2022-08-20T10:05:12.4650041Z             # vs = v' * st / a
2022-08-20T10:05:12.4650311Z             vs = blas.dot(v, s, offsety = ind, n = m)
2022-08-20T10:05:12.4650549Z     
2022-08-20T10:05:12.4651253Z             # vz = v' * J *zt / b
2022-08-20T10:05:12.4651724Z             vz = jdot(v, z, offsety = ind, n = m)
2022-08-20T10:05:12.4651960Z     
2022-08-20T10:05:12.4652334Z             # vq = v' * q where q = (st/a + J * zt/b) / (2 * c)
2022-08-20T10:05:12.4652810Z             vq = (vs + vz ) / 2.0 / cc
2022-08-20T10:05:12.4653032Z     
2022-08-20T10:05:12.4653355Z             # vu = v' * u  where u =  st/a - J * zt/b
2022-08-20T10:05:12.4653655Z             vu = vs - vz
2022-08-20T10:05:12.4653865Z     
2022-08-20T10:05:12.4654053Z             # lambda_k0 = c
2022-08-20T10:05:12.4654285Z             lmbda[ind] = cc
2022-08-20T10:05:12.4654602Z     
2022-08-20T10:05:12.4654873Z             # wk0 = 2 * vk0 * (vk' * q) - q0
2022-08-20T10:05:12.4655466Z             wk0 = 2 * v[0] * vq - ( s[ind] + z[ind] ) / 2.0 / cc
2022-08-20T10:05:12.4655699Z     
2022-08-20T10:05:12.4655989Z             # d = (v[0] * (vk' * u) - u0/2) / (wk0 + 1)
2022-08-20T10:05:12.4656492Z             dd = (v[0] * vu - s[ind]/2.0 + z[ind]/2.0) / (wk0 + 1.0)
2022-08-20T10:05:12.4656755Z     
2022-08-20T10:05:12.4657096Z             # lambda_k1 = 2 * v_k1 * vk' * (-d*q + u/2) - d*q1 + u1/2
2022-08-20T10:05:12.4657651Z             blas.copy(v, lmbda, offsetx = 1, offsety = ind+1, n = m-1)
2022-08-20T10:05:12.4658095Z             blas.scal(2.0 * (-dd * vq + 0.5 * vu), lmbda, offset = ind+1,
2022-08-20T10:05:12.4658422Z                n = m-1)
2022-08-20T10:05:12.4658904Z             blas.axpy(s, lmbda, 0.5 * (1.0 - dd/cc), offsetx = ind+1, offsety
2022-08-20T10:05:12.4659242Z                = ind+1, n = m-1)
2022-08-20T10:05:12.4659529Z             blas.axpy(z, lmbda, 0.5 * (1.0 + dd/cc), offsetx = ind+1, offsety
2022-08-20T10:05:12.4659850Z                = ind+1, n = m-1)
2022-08-20T10:05:12.4660056Z     
2022-08-20T10:05:12.4660408Z             # Scale so that sqrt(lambda_k' * J * lambda_k) = sqrt(aa*bb).
2022-08-20T10:05:12.4660855Z             blas.scal(math.sqrt(aa*bb), lmbda, offset = ind, n = m)
2022-08-20T10:05:12.4661118Z     
2022-08-20T10:05:12.4661396Z             # v := (2*v*v' - J) * q
2022-08-20T10:05:12.4661843Z             #    = 2 * (v'*q) * v' - (J* st/a + zt/b) / (2*c)
2022-08-20T10:05:12.4662220Z             blas.scal(2.0 * vq, v)
2022-08-20T10:05:12.4662532Z             v[0] -= s[ind] / 2.0 / cc
2022-08-20T10:05:12.4662939Z             blas.axpy(s, v,  0.5/cc, offsetx = ind+1, offsety = 1, n = m-1)
2022-08-20T10:05:12.4663341Z             blas.axpy(z, v, -0.5/cc, offsetx = ind, n = m)
2022-08-20T10:05:12.4663589Z     
2022-08-20T10:05:12.4663824Z             # v := v^{1/2} = 1/sqrt(2 * (v0 + 1)) * (v + e)
2022-08-20T10:05:12.4664050Z             v[0] += 1.0
2022-08-20T10:05:12.4664310Z             blas.scal(1.0 / math.sqrt(2.0 * v[0]), v)
2022-08-20T10:05:12.4664548Z     
2022-08-20T10:05:12.4664751Z             # beta[k] *= ( aa / bb )**1/2
2022-08-20T10:05:12.4665097Z             W['beta'][k] *= math.sqrt( aa / bb )
2022-08-20T10:05:12.4665331Z     
2022-08-20T10:05:12.4665510Z             ind += m
2022-08-20T10:05:12.4665708Z     
2022-08-20T10:05:12.4665895Z     
2022-08-20T10:05:12.4666158Z         # 's' blocks
2022-08-20T10:05:12.4666360Z         #
2022-08-20T10:05:12.4666633Z         # Let st, zt be the updated variables in the old scaling:
2022-08-20T10:05:12.4666886Z         #
2022-08-20T10:05:12.4667184Z         #     st = Ls * Ls', zt = Lz * Lz'.
2022-08-20T10:05:12.4667411Z         #
2022-08-20T10:05:12.4667735Z         # where Ls and Lz are the 's' components of s, z.
2022-08-20T10:05:12.4667992Z         #
2022-08-20T10:05:12.4668302Z         # 1.  SVD Lz'*Ls = Uk * lambda_k^+ * Vk'.
2022-08-20T10:05:12.4668526Z         #
2022-08-20T10:05:12.4668739Z         # 2.  New scaling is
2022-08-20T10:05:12.4668957Z         #
2022-08-20T10:05:12.4669289Z         #         r[k] := r[k] * Ls * Vk * diag(lambda_k^+)^{-1/2}
2022-08-20T10:05:12.4669698Z         #         rti[k] := r[k] * Lz * Uk * diag(lambda_k^+)^{-1/2}.
2022-08-20T10:05:12.4669951Z         #
2022-08-20T10:05:12.4670124Z     
2022-08-20T10:05:12.4670493Z         work = matrix(0.0, (max( [0] + [r.size[0] for r in W['r']])**2, 1))
2022-08-20T10:05:12.4671033Z         ind = mnl + ml + sum([ len(v) for v in W['v'] ])
2022-08-20T10:05:12.4671299Z         ind2, ind3 = ind, 0
2022-08-20T10:05:12.4671594Z         for k in range(len(W['r'])):
2022-08-20T10:05:12.4671924Z             r, rti = W['r'][k], W['rti'][k]
2022-08-20T10:05:12.4672164Z             m = r.size[0]
2022-08-20T10:05:12.4672356Z     
2022-08-20T10:05:12.4672564Z             # r := r*sk = r*Ls
2022-08-20T10:05:12.4672858Z             blas.gemm(r, s, work, m = m, n = m, k = m, ldB = m, ldC = m,
2022-08-20T10:05:12.4673126Z                 offsetB = ind2)
2022-08-20T10:05:12.4673545Z             blas.copy(work, r, n = m**2)
2022-08-20T10:05:12.4673885Z     
2022-08-20T10:05:12.4674070Z             # rti := rti*zk = rti*Lz
2022-08-20T10:05:12.4674352Z             blas.gemm(rti, z, work, m = m, n = m, k = m, ldB = m, ldC = m,
2022-08-20T10:05:12.4674620Z                 offsetB = ind2)
2022-08-20T10:05:12.4674939Z             blas.copy(work, rti, n = m**2)
2022-08-20T10:05:12.4675176Z     
2022-08-20T10:05:12.4696294Z             # SVD Lz'*Ls = U * lmbds^+ * V'; store U in sk and V' in zk.
2022-08-20T10:05:12.4696880Z             blas.gemm(z, s, work, transA = 'T', m = m, n = m, k = m, ldA = m,
2022-08-20T10:05:12.4697207Z                 ldB = m, ldC = m, offsetA = ind2, offsetB = ind2)
2022-08-20T10:05:12.4697652Z             lapack.gesvd(work, lmbda, jobu = 'A', jobvt = 'A', m = m, n = m,
2022-08-20T10:05:12.4698006Z                 ldA = m, U = s, Vt = z, ldU = m, ldVt = m, offsetS = ind,
2022-08-20T10:05:12.4698319Z                 offsetU = ind2, offsetVt = ind2)
2022-08-20T10:05:12.4698543Z     
2022-08-20T10:05:12.4698741Z             # r := r*V
2022-08-20T10:05:12.4699127Z             blas.gemm(r, z, work, transB = 'T', m = m, n = m, k = m, ldB = m,
2022-08-20T10:05:12.4699410Z                 ldC = m, offsetB = ind2)
2022-08-20T10:05:12.4699675Z             blas.copy(work, r, n = m**2)
2022-08-20T10:05:12.4700021Z     
2022-08-20T10:05:12.4700204Z             # rti := rti*U
2022-08-20T10:05:12.4700602Z             blas.gemm(rti, s, work, n = m, m = m, k = m, ldB = m, ldC = m,
2022-08-20T10:05:12.4700867Z                 offsetB = ind2)
2022-08-20T10:05:12.4701098Z             blas.copy(work, rti, n = m**2)
2022-08-20T10:05:12.4701318Z     
2022-08-20T10:05:12.4701645Z             # r := r*lambda^{-1/2}; rti := rti*lambda^{-1/2}
2022-08-20T10:05:12.4702010Z             for i in range(m):
2022-08-20T10:05:12.4702263Z >               a = 1.0 / math.sqrt(lmbda[ind+i])
2022-08-20T10:05:12.4702566Z E               ZeroDivisionError: float division by zero
2022-08-20T10:05:12.4702754Z 
2022-08-20T10:05:12.4703110Z /usr/share/miniconda3/envs/test-env/lib/python3.10/site-packages/cvxopt/misc.py:628: ZeroDivisionError

Originally posted by @bnavigator in #759 (comment)
@bnavigator
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Attn @Mark-Yeatman

@bnavigator bnavigator mentioned this issue Aug 20, 2022
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@Mark-Yeatman
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I ran the passivity tests for this branch 10 times on my local machine (Windows), couldn't reproduce. We could just remove the test case?

@bnavigator
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The CI failures are on Ubuntu with conda-forge packages.

The parameter set was included for a purpose. Is it meaningful? Necessary to test?

@Mark-Yeatman
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Yeah, I was trying to set something of an expectation about the relative scaling / condition number of the A and D matrices. I'll give it a look on ubuntu tonight.

@Mark-Yeatman
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Looks like this hasn't reared its head again (at least according to the "actions" tab in GitHub), and I still can't reproduce locally. Do we want to close this?

@murrayrm
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Actually, this error continues to be an issue. I've had to re-run most of the recent CI runs on failed tests and this was the error that showed up. I'm not sure there is a fix, but we should at least leave this open to keep track of the fact that there is an outstanding issue.

(I have to admit to being confused about why the error shows up randomly; what is the change in underlying state that leads to an error sometimes but not others?)

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The only difference I can imagine is that the job lands on a different worker in the GitHub Actions backend.

@murrayrm murrayrm mentioned this issue Sep 9, 2022
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bnavigator added a commit to bnavigator/python-control that referenced this issue Oct 8, 2022
@bnavigator
xfail ill-conditioned passive test (issue python-control#761)
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sawyerbfuller pushed a commit to sawyerbfuller/python-control that referenced this issue Nov 8, 2022
@bnavigator @sawyerbfuller
xfail ill-conditioned passive test (issue python-control#761)
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@murrayrm
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I've not seen this issue pop up in a while. We can probably close unless someone has experienced a problem.

@bnavigator
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The test ist marked XFAIL and still fails occasionally.

From yesterday: https://github.com/python-control/python-control/actions/runs/3502370893/jobs/5866722552#step:6:997

@Mark-Yeatman Mark-Yeatman mentioned this issue Dec 12, 2022
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Mark-Yeatman commented Dec 12, 2022 •
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Sorry to let this sit so long, made a pull request to fix. #814

My expectation is that now if we get a weird worker is that one of the edge cases will fail to be correctly categorized as passive instead of getting that division by zero error. ( The underlying reason could be 32bit vs 64 bit?)

@murrayrm murrayrm linked a pull request Dec 16, 2022 that will close this issue
Removed epsilon perturbation value in solve_passivity_LMI. Fix associated unit test. #814
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