Nevertheless, here is the Clarabel-only reproduction of the final solve which now results in Terminated with status = InsufficientProgress
.
import clarabel
import numpy as np
from scipy import sparse
q = [1.8435820570082377, 1.8435820570082377, 2.7653730855123553, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
b = [-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
P = sparse.csc_matrix((81, 81))
data = [-4.6713756474581505, -7.366046824401246e-16, -0.33482275518489235, 1.248525969168963e-19, 2.6328516974490616e-09, -3.9681215422316167e-16, -0.9587196352099683, -2.8174889909481824e-16, 2.6184498937672173e-07, -0.10625452869742728, 1.4101154172740503e-16, 0.5484683093767915, -1, 2.031287962092983e-21, -0.2742382564473529, -2.5439681835905437e-10, -1, -4.6713756474581505, -7.366046824401246e-16, -0.33482275518489235, 1.248525969168963e-19, 2.6328516974490616e-09, -4.6713756474581505, -1.1334168366632864e-15, -2.293542390394861, -2.8162404649790135e-16, 2.644778410741708e-07, 4.6713756474581505, 3.397925282169629e-16, 0.2698485912774968, -1.408622099643301e-16, 0.5484685685889291, 3.9681215422316167e-16, 2.523840753970691, 1.1593671545503101e-15, 0.6090525462693416, -1.248525969168963e-19, -2.6328516974490616e-09, 0.10625452869742728, 2.5580264378371873e-16, -0.8639869308685729, 2.8174889909481824e-16, -2.6184498937672173e-07, -2.031287962092983e-21, 0.38049278489038335, -1.4101154172740503e-16, -0.5484683093767915, 2.5439681835905437e-10, -2.031287962092983e-21, 0.2742382564473529, 2.5439681835905437e-10, -1.4142135623730951, -4.6713756474581505, -7.366046824401246e-16, -1.3348227551848924, 1.248525969168963e-19, 2.6328516974490616e-09, 4.6713756474581505, 3.397925282169629e-16, 0.37610311997492407, -2.8187375169173514e-16, 2.5921213767927265e-07, 3.9681215422316167e-16, 3.523840753970692, 1.159365123262348e-15, 0.8832908027166945, -1.248525969168963e-19, -2.6328516974490616e-09, 0.10625452869742728, 2.5580264378371873e-16, -0.8639869306141761, 2.8174889909481824e-16, -2.6184498937672173e-07, -2.031287962092983e-21, 0.38049278489038335, -1.4101154172740503e-16, -0.5484683093767915, 2.5439681835905437e-10, -2.031287962092983e-21, 0.2742382564473529, 2.5439681835905437e-10, -1, 1, -1, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 1, -1, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 1, -1, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 1, -1, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 1, -1, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 1, -1, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 1, -1, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 1, -1, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 1, -1, 2, -1.4142135623730951, 2, -1.4142135623730951, 2, -1.4142135623730951, 1, -1, 2, -1.4142135623730951, 2, -1.4142135623730951, 1, -1, 2, -1.4142135623730951, 1, -1]
rows = [5, 6, 7, 8, 9, 12, 13, 14, 15, 19, 20, 21, 24, 25, 26, 30, 115, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 116, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 28, 30, 31, 32, 35, 117, 17, 37, 10, 38, 23, 40, 16, 43, 9, 47, 28, 52, 22, 58, 15, 65, 8, 73, 27, 82, 21, 92, 14, 103, 4, 39, 16, 41, 9, 44, 3, 48, 22, 53, 15, 59, 8, 66, 2, 74, 21, 83, 14, 93, 7, 104, 28, 42, 22, 45, 15, 49, 32, 54, 27, 60, 21, 67, 14, 75, 31, 84, 26, 94, 20, 105, 15, 46, 8, 50, 27, 55, 21, 61, 14, 68, 7, 76, 26, 85, 20, 95, 13, 106, 2, 51, 21, 56, 14, 62, 7, 69, 1, 77, 20, 86, 13, 96, 6, 107, 35, 57, 31, 63, 26, 70, 20, 78, 34, 87, 30, 97, 25, 108, 26, 64, 20, 71, 13, 79, 30, 88, 25, 98, 19, 109, 13, 72, 6, 80, 25, 89, 19, 99, 12, 110, 0, 81, 19, 90, 12, 100, 5, 111, 33, 91, 29, 101, 24, 112, 24, 102, 18, 113, 11, 114]
cols = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12, 13, 13, 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 20, 20, 21, 21, 22, 22, 23, 23, 24, 24, 25, 25, 26, 26, 27, 27, 28, 28, 29, 29, 30, 30, 31, 31, 32, 32, 33, 33, 34, 34, 35, 35, 36, 36, 37, 37, 38, 38, 39, 39, 40, 40, 41, 41, 42, 42, 43, 43, 44, 44, 45, 45, 46, 46, 47, 47, 48, 48, 49, 49, 50, 50, 51, 51, 52, 52, 53, 53, 54, 54, 55, 55, 56, 56, 57, 57, 58, 58, 59, 59, 60, 60, 61, 61, 62, 62, 63, 63, 64, 64, 65, 65, 66, 66, 67, 67, 68, 68, 69, 69, 70, 70, 71, 71, 72, 72, 73, 73, 74, 74, 75, 75, 76, 76, 77, 77, 78, 78, 79, 79, 80, 80]
A = sparse.csc_matrix((data, (rows, cols)))
cones = [
clarabel.ZeroConeT(37),
clarabel.NonnegativeConeT(0),
clarabel.PSDTriangleConeT(12),
clarabel.PSDTriangleConeT(2),
]
settings = clarabel.DefaultSettings()
solver = clarabel.DefaultSolver(P, q, A, b, cones, settings)
solver.solve()
I know at least one relatively simple thing I can do to improve the numerics for this problem, but thought I should report the regression before I do.