31 October 2024 08:33:35 AM SGMGA_VCN_PRB C++ version Test the SGMGA_VCN and SGMGA_VCN_ORDERED functions. SGMGA_VCN_TESTS calls SGMGA_VCN_TEST. SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 1 LEVEL_WEIGHT: 1 1 SGMGA_VCN_NAIVE I Q X MIN -2 0 0 1 0 0 0 MAX 0 0 0 SGMGA_VCN I Q X MIN -2 0 0 1 0 0 0 MAX 0 0 0 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 1 LEVEL_WEIGHT: 1 1 SGMGA_VCN_NAIVE I Q X MIN -1 0 0 1 0 0 0 2 1 1 0 3 1 0 1 MAX 1 1 1 SGMGA_VCN I Q X MIN -1 0 0 1 0 0 0 2 1 1 0 3 1 0 1 MAX 1 1 1 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 1 LEVEL_WEIGHT: 1 1 SGMGA_VCN_NAIVE I Q X MIN 0 0 0 1 1 1 0 2 2 2 0 3 1 0 1 4 2 1 1 5 2 0 2 MAX 2 2 2 SGMGA_VCN I Q X MIN 0 0 0 1 1 1 0 2 2 2 0 3 1 0 1 4 2 1 1 5 2 0 2 MAX 2 2 2 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 1 LEVEL_WEIGHT: 1 1 SGMGA_VCN_NAIVE I Q X MIN 1 0 0 1 2 2 0 2 3 3 0 3 2 1 1 4 3 2 1 5 2 0 2 6 3 1 2 7 3 0 3 MAX 3 3 3 SGMGA_VCN I Q X MIN 1 0 0 1 2 2 0 2 3 3 0 3 2 1 1 4 3 2 1 5 2 0 2 6 3 1 2 7 3 0 3 MAX 3 3 3 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 1 LEVEL_WEIGHT: 1 1 SGMGA_VCN_NAIVE I Q X MIN 2 0 0 1 3 3 0 2 4 4 0 3 3 2 1 4 4 3 1 5 3 1 2 6 4 2 2 7 3 0 3 8 4 1 3 9 4 0 4 MAX 4 4 4 SGMGA_VCN I Q X MIN 2 0 0 1 3 3 0 2 4 4 0 3 3 2 1 4 4 3 1 5 3 1 2 6 4 2 2 7 3 0 3 8 4 1 3 9 4 0 4 MAX 4 4 4 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 1 1 LEVEL_WEIGHT: 1 1 1 SGMGA_VCN_NAIVE I Q X MIN -3 0 0 0 1 0 0 0 0 MAX 0 0 0 0 SGMGA_VCN I Q X MIN -3 0 0 0 1 0 0 0 0 MAX 0 0 0 0 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 1 1 LEVEL_WEIGHT: 1 1 1 SGMGA_VCN_NAIVE I Q X MIN -2 0 0 0 1 0 0 0 0 2 1 1 0 0 3 1 0 1 0 4 1 0 0 1 MAX 1 1 1 1 SGMGA_VCN I Q X MIN -2 0 0 0 1 0 0 0 0 2 1 1 0 0 3 1 0 1 0 4 1 0 0 1 MAX 1 1 1 1 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 1 1 LEVEL_WEIGHT: 1 1 1 SGMGA_VCN_NAIVE I Q X MIN -1 0 0 0 1 0 0 0 0 2 1 1 0 0 3 2 2 0 0 4 1 0 1 0 5 2 1 1 0 6 2 0 2 0 7 1 0 0 1 8 2 1 0 1 9 2 0 1 1 10 2 0 0 2 MAX 2 2 2 2 SGMGA_VCN I Q X MIN -1 0 0 0 1 0 0 0 0 2 1 1 0 0 3 2 2 0 0 4 1 0 1 0 5 2 1 1 0 6 2 0 2 0 7 1 0 0 1 8 2 1 0 1 9 2 0 1 1 10 2 0 0 2 MAX 2 2 2 2 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 1 1 LEVEL_WEIGHT: 1 1 1 SGMGA_VCN_NAIVE I Q X MIN 0 0 0 0 1 1 1 0 0 2 2 2 0 0 3 3 3 0 0 4 1 0 1 0 5 2 1 1 0 6 3 2 1 0 7 2 0 2 0 8 3 1 2 0 9 3 0 3 0 10 1 0 0 1 11 2 1 0 1 12 3 2 0 1 13 2 0 1 1 14 3 1 1 1 15 3 0 2 1 16 2 0 0 2 17 3 1 0 2 18 3 0 1 2 19 3 0 0 3 MAX 3 3 3 3 SGMGA_VCN I Q X MIN 0 0 0 0 1 1 1 0 0 2 2 2 0 0 3 3 3 0 0 4 1 0 1 0 5 2 1 1 0 6 3 2 1 0 7 2 0 2 0 8 3 1 2 0 9 3 0 3 0 10 1 0 0 1 11 2 1 0 1 12 3 2 0 1 13 2 0 1 1 14 3 1 1 1 15 3 0 2 1 16 2 0 0 2 17 3 1 0 2 18 3 0 1 2 19 3 0 0 3 MAX 3 3 3 3 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 1 1 LEVEL_WEIGHT: 1 1 1 SGMGA_VCN_NAIVE I Q X MIN 1 0 0 0 1 2 2 0 0 2 3 3 0 0 3 4 4 0 0 4 2 1 1 0 5 3 2 1 0 6 4 3 1 0 7 2 0 2 0 8 3 1 2 0 9 4 2 2 0 10 3 0 3 0 11 4 1 3 0 12 4 0 4 0 13 2 1 0 1 14 3 2 0 1 15 4 3 0 1 16 2 0 1 1 17 3 1 1 1 18 4 2 1 1 19 3 0 2 1 20 4 1 2 1 21 4 0 3 1 22 2 0 0 2 23 3 1 0 2 24 4 2 0 2 25 3 0 1 2 26 4 1 1 2 27 4 0 2 2 28 3 0 0 3 29 4 1 0 3 30 4 0 1 3 31 4 0 0 4 MAX 4 4 4 4 SGMGA_VCN I Q X MIN 1 0 0 0 1 2 2 0 0 2 3 3 0 0 3 4 4 0 0 4 2 1 1 0 5 3 2 1 0 6 4 3 1 0 7 2 0 2 0 8 3 1 2 0 9 4 2 2 0 10 3 0 3 0 11 4 1 3 0 12 4 0 4 0 13 2 1 0 1 14 3 2 0 1 15 4 3 0 1 16 2 0 1 1 17 3 1 1 1 18 4 2 1 1 19 3 0 2 1 20 4 1 2 1 21 4 0 3 1 22 2 0 0 2 23 3 1 0 2 24 4 2 0 2 25 3 0 1 2 26 4 1 1 2 27 4 0 2 2 28 3 0 0 3 29 4 1 0 3 30 4 0 1 3 31 4 0 0 4 MAX 4 4 4 4 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 1 1 1 LEVEL_WEIGHT: 1 1 1 1 SGMGA_VCN_NAIVE I Q X MIN -2 0 0 0 0 1 0 0 0 0 0 2 1 1 0 0 0 3 2 2 0 0 0 4 1 0 1 0 0 5 2 1 1 0 0 6 2 0 2 0 0 7 1 0 0 1 0 8 2 1 0 1 0 9 2 0 1 1 0 10 2 0 0 2 0 11 1 0 0 0 1 12 2 1 0 0 1 13 2 0 1 0 1 14 2 0 0 1 1 15 2 0 0 0 2 MAX 2 2 2 2 2 SGMGA_VCN I Q X MIN -2 0 0 0 0 1 0 0 0 0 0 2 1 1 0 0 0 3 2 2 0 0 0 4 1 0 1 0 0 5 2 1 1 0 0 6 2 0 2 0 0 7 1 0 0 1 0 8 2 1 0 1 0 9 2 0 1 1 0 10 2 0 0 2 0 11 1 0 0 0 1 12 2 1 0 0 1 13 2 0 1 0 1 14 2 0 0 1 1 15 2 0 0 0 2 MAX 2 2 2 2 2 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 1 1 1 LEVEL_WEIGHT: 1 1 1 1 SGMGA_VCN_NAIVE I Q X MIN -1 0 0 0 0 1 0 0 0 0 0 2 1 1 0 0 0 3 2 2 0 0 0 4 3 3 0 0 0 5 1 0 1 0 0 6 2 1 1 0 0 7 3 2 1 0 0 8 2 0 2 0 0 9 3 1 2 0 0 10 3 0 3 0 0 11 1 0 0 1 0 12 2 1 0 1 0 13 3 2 0 1 0 14 2 0 1 1 0 15 3 1 1 1 0 16 3 0 2 1 0 17 2 0 0 2 0 18 3 1 0 2 0 19 3 0 1 2 0 20 3 0 0 3 0 21 1 0 0 0 1 22 2 1 0 0 1 23 3 2 0 0 1 24 2 0 1 0 1 25 3 1 1 0 1 26 3 0 2 0 1 27 2 0 0 1 1 28 3 1 0 1 1 29 3 0 1 1 1 30 3 0 0 2 1 31 2 0 0 0 2 32 3 1 0 0 2 33 3 0 1 0 2 34 3 0 0 1 2 35 3 0 0 0 3 MAX 3 3 3 3 3 SGMGA_VCN I Q X MIN -1 0 0 0 0 1 0 0 0 0 0 2 1 1 0 0 0 3 2 2 0 0 0 4 3 3 0 0 0 5 1 0 1 0 0 6 2 1 1 0 0 7 3 2 1 0 0 8 2 0 2 0 0 9 3 1 2 0 0 10 3 0 3 0 0 11 1 0 0 1 0 12 2 1 0 1 0 13 3 2 0 1 0 14 2 0 1 1 0 15 3 1 1 1 0 16 3 0 2 1 0 17 2 0 0 2 0 18 3 1 0 2 0 19 3 0 1 2 0 20 3 0 0 3 0 21 1 0 0 0 1 22 2 1 0 0 1 23 3 2 0 0 1 24 2 0 1 0 1 25 3 1 1 0 1 26 3 0 2 0 1 27 2 0 0 1 1 28 3 1 0 1 1 29 3 0 1 1 1 30 3 0 0 2 1 31 2 0 0 0 2 32 3 1 0 0 2 33 3 0 1 0 2 34 3 0 0 1 2 35 3 0 0 0 3 MAX 3 3 3 3 3 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 0 1 LEVEL_WEIGHT: 1 0 1 SGMGA_VCN_NAIVE I Q X MIN 0 0 0 0 1 1 1 0 0 2 2 2 0 0 3 1 0 0 1 4 2 1 0 1 5 2 0 0 2 MAX 2 2 0 2 SGMGA_VCN I Q X MIN 0 0 0 0 1 1 1 0 0 2 2 2 0 0 3 1 0 0 1 4 2 1 0 1 5 2 0 0 2 MAX 2 2 0 2 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 2 LEVEL_WEIGHT: 1 0.5 SGMGA_VCN_NAIVE I Q X MIN -1.5 0 0 1 0 0 0 MAX 0 0 0 SGMGA_VCN I Q X MIN -1.5 0 0 1 0 0 0 MAX 0 0 0 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 2 LEVEL_WEIGHT: 1 0.5 SGMGA_VCN_NAIVE I Q X MIN -0.5 0 0 1 0 0 0 2 1 1 0 3 0.5 0 1 4 1 0 2 MAX 1 1 2 SGMGA_VCN I Q X MIN -0.5 0 0 1 0 0 0 2 1 1 0 3 0.5 0 1 4 1 0 2 MAX 1 1 2 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 2 LEVEL_WEIGHT: 1 0.5 SGMGA_VCN_NAIVE I Q X MIN 0.5 0 0 1 1 1 0 2 2 2 0 3 1.5 1 1 4 1 0 2 5 2 1 2 6 1.5 0 3 7 2 0 4 MAX 2 2 4 SGMGA_VCN I Q X MIN 0.5 0 0 1 1 1 0 2 2 2 0 3 1.5 1 1 4 1 0 2 5 2 1 2 6 1.5 0 3 7 2 0 4 MAX 2 2 4 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 2 LEVEL_WEIGHT: 1 0.5 SGMGA_VCN_NAIVE I Q X MIN 1.5 0 0 1 2 2 0 2 3 3 0 3 2.5 2 1 4 2 1 2 5 3 2 2 6 2.5 1 3 7 2 0 4 8 3 1 4 9 2.5 0 5 10 3 0 6 MAX 3 3 6 SGMGA_VCN I Q X MIN 1.5 0 0 1 2 2 0 2 3 3 0 3 2.5 2 1 4 2 1 2 5 3 2 2 6 2.5 1 3 7 2 0 4 8 3 1 4 9 2.5 0 5 10 3 0 6 MAX 3 3 6 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 2 LEVEL_WEIGHT: 1 0.5 SGMGA_VCN_NAIVE I Q X MIN 2.5 0 0 1 3 3 0 2 4 4 0 3 3.5 3 1 4 3 2 2 5 4 3 2 6 3.5 2 3 7 3 1 4 8 4 2 4 9 3.5 1 5 10 3 0 6 11 4 1 6 12 3.5 0 7 13 4 0 8 MAX 4 4 8 SGMGA_VCN I Q X MIN 2.5 0 0 1 3 3 0 2 4 4 0 3 3.5 3 1 4 3 2 2 5 4 3 2 6 3.5 2 3 7 3 1 4 8 4 2 4 9 3.5 1 5 10 3 0 6 11 4 1 6 12 3.5 0 7 13 4 0 8 MAX 4 4 8 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 2 3 LEVEL_WEIGHT: 1 0.5 0.333333 SGMGA_VCN_NAIVE I Q X MIN -1.83333 0 0 0 1 0 0 0 0 MAX 0 0 0 0 SGMGA_VCN I Q X MIN -1.83333 0 0 0 1 0 0 0 0 MAX 0 0 0 0 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 2 3 LEVEL_WEIGHT: 1 0.5 0.333333 SGMGA_VCN_NAIVE I Q X MIN -0.833333 0 0 0 1 0 0 0 0 2 1 1 0 0 3 0.5 0 1 0 4 1 0 2 0 5 0.333333 0 0 1 6 0.833333 0 1 1 7 0.666667 0 0 2 8 1 0 0 3 MAX 1 1 2 3 SGMGA_VCN I Q X MIN -0.833333 0 0 0 1 0 0 0 0 2 1 1 0 0 3 0.5 0 1 0 4 1 0 2 0 5 0.333333 0 0 1 6 0.833333 0 1 1 7 0.666667 0 0 2 8 1 0 0 3 MAX 1 1 2 3 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 2 3 LEVEL_WEIGHT: 1 0.5 0.333333 SGMGA_VCN_NAIVE I Q X MIN 0.166667 0 0 0 1 1 1 0 0 2 2 2 0 0 3 0.5 0 1 0 4 1.5 1 1 0 5 1 0 2 0 6 2 1 2 0 7 1.5 0 3 0 8 2 0 4 0 9 0.333333 0 0 1 10 1.33333 1 0 1 11 0.833333 0 1 1 12 1.83333 1 1 1 13 1.33333 0 2 1 14 1.83333 0 3 1 15 0.666667 0 0 2 16 1.66667 1 0 2 17 1.16667 0 1 2 18 1.66667 0 2 2 19 1 0 0 3 20 2 1 0 3 21 1.5 0 1 3 22 2 0 2 3 23 1.33333 0 0 4 24 1.83333 0 1 4 25 1.66667 0 0 5 26 2 0 0 6 MAX 2 2 4 6 SGMGA_VCN I Q X MIN 0.166667 0 0 0 1 1 1 0 0 2 2 2 0 0 3 0.5 0 1 0 4 1.5 1 1 0 5 1 0 2 0 6 2 1 2 0 7 1.5 0 3 0 8 2 0 4 0 9 0.333333 0 0 1 10 1.33333 1 0 1 11 0.833333 0 1 1 12 1.83333 1 1 1 13 1.33333 0 2 1 14 1.83333 0 3 1 15 0.666667 0 0 2 16 1.66667 1 0 2 17 1.16667 0 1 2 18 1.66667 0 2 2 19 1 0 0 3 20 2 1 0 3 21 1.5 0 1 3 22 2 0 2 3 23 1.33333 0 0 4 24 1.83333 0 1 4 25 1.66667 0 0 5 26 2 0 0 6 MAX 2 2 4 6 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 2 3 LEVEL_WEIGHT: 1 0.5 0.333333 SGMGA_VCN_NAIVE I Q X MIN 1.16667 0 0 0 1 2 2 0 0 2 3 3 0 0 3 1.5 1 1 0 4 2.5 2 1 0 5 2 1 2 0 6 3 2 2 0 7 1.5 0 3 0 8 2.5 1 3 0 9 2 0 4 0 10 3 1 4 0 11 2.5 0 5 0 12 3 0 6 0 13 1.33333 1 0 1 14 2.33333 2 0 1 15 1.83333 1 1 1 16 2.83333 2 1 1 17 1.33333 0 2 1 18 2.33333 1 2 1 19 1.83333 0 3 1 20 2.83333 1 3 1 21 2.33333 0 4 1 22 2.83333 0 5 1 23 1.66667 1 0 2 24 2.66667 2 0 2 25 2.16667 1 1 2 26 1.66667 0 2 2 27 2.66667 1 2 2 28 2.16667 0 3 2 29 2.66667 0 4 2 30 2 1 0 3 31 3 2 0 3 32 1.5 0 1 3 33 2.5 1 1 3 34 2 0 2 3 35 3 1 2 3 36 2.5 0 3 3 37 3 0 4 3 38 1.33333 0 0 4 39 2.33333 1 0 4 40 1.83333 0 1 4 41 2.83333 1 1 4 42 2.33333 0 2 4 43 2.83333 0 3 4 44 1.66667 0 0 5 45 2.66667 1 0 5 46 2.16667 0 1 5 47 2.66667 0 2 5 48 2 0 0 6 49 3 1 0 6 50 2.5 0 1 6 51 3 0 2 6 52 2.33333 0 0 7 53 2.83333 0 1 7 54 2.66667 0 0 8 55 3 0 0 9 MAX 3 3 6 9 SGMGA_VCN I Q X MIN 1.16667 0 0 0 1 2 2 0 0 2 3 3 0 0 3 1.5 1 1 0 4 2.5 2 1 0 5 2 1 2 0 6 3 2 2 0 7 1.5 0 3 0 8 2.5 1 3 0 9 2 0 4 0 10 3 1 4 0 11 2.5 0 5 0 12 3 0 6 0 13 1.33333 1 0 1 14 2.33333 2 0 1 15 1.83333 1 1 1 16 2.83333 2 1 1 17 1.33333 0 2 1 18 2.33333 1 2 1 19 1.83333 0 3 1 20 2.83333 1 3 1 21 2.33333 0 4 1 22 2.83333 0 5 1 23 1.66667 1 0 2 24 2.66667 2 0 2 25 2.16667 1 1 2 26 1.66667 0 2 2 27 2.66667 1 2 2 28 2.16667 0 3 2 29 2.66667 0 4 2 30 2 1 0 3 31 3 2 0 3 32 1.5 0 1 3 33 2.5 1 1 3 34 2 0 2 3 35 3 1 2 3 36 2.5 0 3 3 37 3 0 4 3 38 1.33333 0 0 4 39 2.33333 1 0 4 40 1.83333 0 1 4 41 2.83333 1 1 4 42 2.33333 0 2 4 43 2.83333 0 3 4 44 1.66667 0 0 5 45 2.66667 1 0 5 46 2.16667 0 1 5 47 2.66667 0 2 5 48 2 0 0 6 49 3 1 0 6 50 2.5 0 1 6 51 3 0 2 6 52 2.33333 0 0 7 53 2.83333 0 1 7 54 2.66667 0 0 8 55 3 0 0 9 MAX 3 3 6 9 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 2 3 LEVEL_WEIGHT: 1 0.5 0.333333 SGMGA_VCN_NAIVE I Q X MIN 2.16667 0 0 0 1 3 3 0 0 2 4 4 0 0 3 2.5 2 1 0 4 3.5 3 1 0 5 3 2 2 0 6 4 3 2 0 7 2.5 1 3 0 8 3.5 2 3 0 9 3 1 4 0 10 4 2 4 0 11 2.5 0 5 0 12 3.5 1 5 0 13 3 0 6 0 14 4 1 6 0 15 3.5 0 7 0 16 4 0 8 0 17 2.33333 2 0 1 18 3.33333 3 0 1 19 2.83333 2 1 1 20 3.83333 3 1 1 21 2.33333 1 2 1 22 3.33333 2 2 1 23 2.83333 1 3 1 24 3.83333 2 3 1 25 2.33333 0 4 1 26 3.33333 1 4 1 27 2.83333 0 5 1 28 3.83333 1 5 1 29 3.33333 0 6 1 30 3.83333 0 7 1 31 2.66667 2 0 2 32 3.66667 3 0 2 33 3.16667 2 1 2 34 2.66667 1 2 2 35 3.66667 2 2 2 36 3.16667 1 3 2 37 2.66667 0 4 2 38 3.66667 1 4 2 39 3.16667 0 5 2 40 3.66667 0 6 2 41 3 2 0 3 42 4 3 0 3 43 2.5 1 1 3 44 3.5 2 1 3 45 3 1 2 3 46 4 2 2 3 47 2.5 0 3 3 48 3.5 1 3 3 49 3 0 4 3 50 4 1 4 3 51 3.5 0 5 3 52 4 0 6 3 53 2.33333 1 0 4 54 3.33333 2 0 4 55 2.83333 1 1 4 56 3.83333 2 1 4 57 2.33333 0 2 4 58 3.33333 1 2 4 59 2.83333 0 3 4 60 3.83333 1 3 4 61 3.33333 0 4 4 62 3.83333 0 5 4 63 2.66667 1 0 5 64 3.66667 2 0 5 65 3.16667 1 1 5 66 2.66667 0 2 5 67 3.66667 1 2 5 68 3.16667 0 3 5 69 3.66667 0 4 5 70 3 1 0 6 71 4 2 0 6 72 2.5 0 1 6 73 3.5 1 1 6 74 3 0 2 6 75 4 1 2 6 76 3.5 0 3 6 77 4 0 4 6 78 2.33333 0 0 7 79 3.33333 1 0 7 80 2.83333 0 1 7 81 3.83333 1 1 7 82 3.33333 0 2 7 83 3.83333 0 3 7 84 2.66667 0 0 8 85 3.66667 1 0 8 86 3.16667 0 1 8 87 3.66667 0 2 8 88 3 0 0 9 89 4 1 0 9 90 3.5 0 1 9 91 4 0 2 9 92 3.33333 0 0 10 93 3.83333 0 1 10 94 3.66667 0 0 11 95 4 0 0 12 MAX 4 4 8 12 SGMGA_VCN I Q X MIN 2.16667 0 0 0 1 3 3 0 0 2 4 4 0 0 3 2.5 2 1 0 4 3.5 3 1 0 5 3 2 2 0 6 4 3 2 0 7 2.5 1 3 0 8 3.5 2 3 0 9 3 1 4 0 10 4 2 4 0 11 2.5 0 5 0 12 3.5 1 5 0 13 3 0 6 0 14 4 1 6 0 15 3.5 0 7 0 16 4 0 8 0 17 2.33333 2 0 1 18 3.33333 3 0 1 19 2.83333 2 1 1 20 3.83333 3 1 1 21 2.33333 1 2 1 22 3.33333 2 2 1 23 2.83333 1 3 1 24 3.83333 2 3 1 25 2.33333 0 4 1 26 3.33333 1 4 1 27 2.83333 0 5 1 28 3.83333 1 5 1 29 3.33333 0 6 1 30 3.83333 0 7 1 31 2.66667 2 0 2 32 3.66667 3 0 2 33 3.16667 2 1 2 34 2.66667 1 2 2 35 3.66667 2 2 2 36 3.16667 1 3 2 37 2.66667 0 4 2 38 3.66667 1 4 2 39 3.16667 0 5 2 40 3.66667 0 6 2 41 3 2 0 3 42 4 3 0 3 43 2.5 1 1 3 44 3.5 2 1 3 45 3 1 2 3 46 4 2 2 3 47 2.5 0 3 3 48 3.5 1 3 3 49 3 0 4 3 50 4 1 4 3 51 3.5 0 5 3 52 4 0 6 3 53 2.33333 1 0 4 54 3.33333 2 0 4 55 2.83333 1 1 4 56 3.83333 2 1 4 57 2.33333 0 2 4 58 3.33333 1 2 4 59 2.83333 0 3 4 60 3.83333 1 3 4 61 3.33333 0 4 4 62 3.83333 0 5 4 63 2.66667 1 0 5 64 3.66667 2 0 5 65 3.16667 1 1 5 66 2.66667 0 2 5 67 3.66667 1 2 5 68 3.16667 0 3 5 69 3.66667 0 4 5 70 3 1 0 6 71 4 2 0 6 72 2.5 0 1 6 73 3.5 1 1 6 74 3 0 2 6 75 4 1 2 6 76 3.5 0 3 6 77 4 0 4 6 78 2.33333 0 0 7 79 3.33333 1 0 7 80 2.83333 0 1 7 81 3.83333 1 1 7 82 3.33333 0 2 7 83 3.83333 0 3 7 84 2.66667 0 0 8 85 3.66667 1 0 8 86 3.16667 0 1 8 87 3.66667 0 2 8 88 3 0 0 9 89 4 1 0 9 90 3.5 0 1 9 91 4 0 2 9 92 3.33333 0 0 10 93 3.83333 0 1 10 94 3.66667 0 0 11 95 4 0 0 12 MAX 4 4 8 12 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 2 3 4 LEVEL_WEIGHT: 1 0.5 0.333333 0.25 SGMGA_VCN_NAIVE I Q X MIN -0.0833333 0 0 0 0 1 0 0 0 0 0 2 1 1 0 0 0 3 2 2 0 0 0 4 0.5 0 1 0 0 5 1.5 1 1 0 0 6 1 0 2 0 0 7 2 1 2 0 0 8 1.5 0 3 0 0 9 2 0 4 0 0 10 0.333333 0 0 1 0 11 1.33333 1 0 1 0 12 0.833333 0 1 1 0 13 1.83333 1 1 1 0 14 1.33333 0 2 1 0 15 1.83333 0 3 1 0 16 0.666667 0 0 2 0 17 1.66667 1 0 2 0 18 1.16667 0 1 2 0 19 1.66667 0 2 2 0 20 1 0 0 3 0 21 2 1 0 3 0 22 1.5 0 1 3 0 23 2 0 2 3 0 24 1.33333 0 0 4 0 25 1.83333 0 1 4 0 26 1.66667 0 0 5 0 27 2 0 0 6 0 28 0.25 0 0 0 1 29 1.25 1 0 0 1 30 0.75 0 1 0 1 31 1.75 1 1 0 1 32 1.25 0 2 0 1 33 1.75 0 3 0 1 34 0.583333 0 0 1 1 35 1.58333 1 0 1 1 36 1.08333 0 1 1 1 37 1.58333 0 2 1 1 38 0.916667 0 0 2 1 39 1.91667 1 0 2 1 40 1.41667 0 1 2 1 41 1.91667 0 2 2 1 42 1.25 0 0 3 1 43 1.75 0 1 3 1 44 1.58333 0 0 4 1 45 1.91667 0 0 5 1 46 0.5 0 0 0 2 47 1.5 1 0 0 2 48 1 0 1 0 2 49 2 1 1 0 2 50 1.5 0 2 0 2 51 2 0 3 0 2 52 0.833333 0 0 1 2 53 1.83333 1 0 1 2 54 1.33333 0 1 1 2 55 1.83333 0 2 1 2 56 1.16667 0 0 2 2 57 1.66667 0 1 2 2 58 1.5 0 0 3 2 59 2 0 1 3 2 60 1.83333 0 0 4 2 61 0.75 0 0 0 3 62 1.75 1 0 0 3 63 1.25 0 1 0 3 64 1.75 0 2 0 3 65 1.08333 0 0 1 3 66 1.58333 0 1 1 3 67 1.41667 0 0 2 3 68 1.91667 0 1 2 3 69 1.75 0 0 3 3 70 1 0 0 0 4 71 2 1 0 0 4 72 1.5 0 1 0 4 73 2 0 2 0 4 74 1.33333 0 0 1 4 75 1.83333 0 1 1 4 76 1.66667 0 0 2 4 77 2 0 0 3 4 78 1.25 0 0 0 5 79 1.75 0 1 0 5 80 1.58333 0 0 1 5 81 1.91667 0 0 2 5 82 1.5 0 0 0 6 83 2 0 1 0 6 84 1.83333 0 0 1 6 85 1.75 0 0 0 7 86 2 0 0 0 8 MAX 2 2 4 6 8 SGMGA_VCN I Q X MIN -0.0833333 0 0 0 0 1 0 0 0 0 0 2 1 1 0 0 0 3 2 2 0 0 0 4 0.5 0 1 0 0 5 1.5 1 1 0 0 6 1 0 2 0 0 7 2 1 2 0 0 8 1.5 0 3 0 0 9 2 0 4 0 0 10 0.333333 0 0 1 0 11 1.33333 1 0 1 0 12 0.833333 0 1 1 0 13 1.83333 1 1 1 0 14 1.33333 0 2 1 0 15 1.83333 0 3 1 0 16 0.666667 0 0 2 0 17 1.66667 1 0 2 0 18 1.16667 0 1 2 0 19 1.66667 0 2 2 0 20 1 0 0 3 0 21 2 1 0 3 0 22 1.5 0 1 3 0 23 2 0 2 3 0 24 1.33333 0 0 4 0 25 1.83333 0 1 4 0 26 1.66667 0 0 5 0 27 2 0 0 6 0 28 0.25 0 0 0 1 29 1.25 1 0 0 1 30 0.75 0 1 0 1 31 1.75 1 1 0 1 32 1.25 0 2 0 1 33 1.75 0 3 0 1 34 0.583333 0 0 1 1 35 1.58333 1 0 1 1 36 1.08333 0 1 1 1 37 1.58333 0 2 1 1 38 0.916667 0 0 2 1 39 1.91667 1 0 2 1 40 1.41667 0 1 2 1 41 1.91667 0 2 2 1 42 1.25 0 0 3 1 43 1.75 0 1 3 1 44 1.58333 0 0 4 1 45 1.91667 0 0 5 1 46 0.5 0 0 0 2 47 1.5 1 0 0 2 48 1 0 1 0 2 49 2 1 1 0 2 50 1.5 0 2 0 2 51 2 0 3 0 2 52 0.833333 0 0 1 2 53 1.83333 1 0 1 2 54 1.33333 0 1 1 2 55 1.83333 0 2 1 2 56 1.16667 0 0 2 2 57 1.66667 0 1 2 2 58 1.5 0 0 3 2 59 2 0 1 3 2 60 1.83333 0 0 4 2 61 0.75 0 0 0 3 62 1.75 1 0 0 3 63 1.25 0 1 0 3 64 1.75 0 2 0 3 65 1.08333 0 0 1 3 66 1.58333 0 1 1 3 67 1.41667 0 0 2 3 68 1.91667 0 1 2 3 69 1.75 0 0 3 3 70 1 0 0 0 4 71 2 1 0 0 4 72 1.5 0 1 0 4 73 2 0 2 0 4 74 1.33333 0 0 1 4 75 1.83333 0 1 1 4 76 1.66667 0 0 2 4 77 2 0 0 3 4 78 1.25 0 0 0 5 79 1.75 0 1 0 5 80 1.58333 0 0 1 5 81 1.91667 0 0 2 5 82 1.5 0 0 0 6 83 2 0 1 0 6 84 1.83333 0 0 1 6 85 1.75 0 0 0 7 86 2 0 0 0 8 MAX 2 2 4 6 8 SGMGA_VCN_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we just compare the results. IMPORTANCE: 1 2 3 4 LEVEL_WEIGHT: 1 0.5 0.333333 0.25 SGMGA_VCN_NAIVE I Q X MIN 0.916667 0 0 0 0 1 1 1 0 0 0 2 2 2 0 0 0 3 3 3 0 0 0 4 1.5 1 1 0 0 5 2.5 2 1 0 0 6 1 0 2 0 0 7 2 1 2 0 0 8 3 2 2 0 0 9 1.5 0 3 0 0 10 2.5 1 3 0 0 11 2 0 4 0 0 12 3 1 4 0 0 13 2.5 0 5 0 0 14 3 0 6 0 0 15 1.33333 1 0 1 0 16 2.33333 2 0 1 0 17 1.83333 1 1 1 0 18 2.83333 2 1 1 0 19 1.33333 0 2 1 0 20 2.33333 1 2 1 0 21 1.83333 0 3 1 0 22 2.83333 1 3 1 0 23 2.33333 0 4 1 0 24 2.83333 0 5 1 0 25 1.66667 1 0 2 0 26 2.66667 2 0 2 0 27 1.16667 0 1 2 0 28 2.16667 1 1 2 0 29 1.66667 0 2 2 0 30 2.66667 1 2 2 0 31 2.16667 0 3 2 0 32 2.66667 0 4 2 0 33 1 0 0 3 0 34 2 1 0 3 0 35 3 2 0 3 0 36 1.5 0 1 3 0 37 2.5 1 1 3 0 38 2 0 2 3 0 39 3 1 2 3 0 40 2.5 0 3 3 0 41 3 0 4 3 0 42 1.33333 0 0 4 0 43 2.33333 1 0 4 0 44 1.83333 0 1 4 0 45 2.83333 1 1 4 0 46 2.33333 0 2 4 0 47 2.83333 0 3 4 0 48 1.66667 0 0 5 0 49 2.66667 1 0 5 0 50 2.16667 0 1 5 0 51 2.66667 0 2 5 0 52 2 0 0 6 0 53 3 1 0 6 0 54 2.5 0 1 6 0 55 3 0 2 6 0 56 2.33333 0 0 7 0 57 2.83333 0 1 7 0 58 2.66667 0 0 8 0 59 3 0 0 9 0 60 1.25 1 0 0 1 61 2.25 2 0 0 1 62 1.75 1 1 0 1 63 2.75 2 1 0 1 64 1.25 0 2 0 1 65 2.25 1 2 0 1 66 1.75 0 3 0 1 67 2.75 1 3 0 1 68 2.25 0 4 0 1 69 2.75 0 5 0 1 70 1.58333 1 0 1 1 71 2.58333 2 0 1 1 72 1.08333 0 1 1 1 73 2.08333 1 1 1 1 74 1.58333 0 2 1 1 75 2.58333 1 2 1 1 76 2.08333 0 3 1 1 77 2.58333 0 4 1 1 78 1.91667 1 0 2 1 79 2.91667 2 0 2 1 80 1.41667 0 1 2 1 81 2.41667 1 1 2 1 82 1.91667 0 2 2 1 83 2.91667 1 2 2 1 84 2.41667 0 3 2 1 85 2.91667 0 4 2 1 86 1.25 0 0 3 1 87 2.25 1 0 3 1 88 1.75 0 1 3 1 89 2.75 1 1 3 1 90 2.25 0 2 3 1 91 2.75 0 3 3 1 92 1.58333 0 0 4 1 93 2.58333 1 0 4 1 94 2.08333 0 1 4 1 95 2.58333 0 2 4 1 96 1.91667 0 0 5 1 97 2.91667 1 0 5 1 98 2.41667 0 1 5 1 99 2.91667 0 2 5 1 100 2.25 0 0 6 1 101 2.75 0 1 6 1 102 2.58333 0 0 7 1 103 2.91667 0 0 8 1 104 1.5 1 0 0 2 105 2.5 2 0 0 2 106 1 0 1 0 2 107 2 1 1 0 2 108 3 2 1 0 2 109 1.5 0 2 0 2 110 2.5 1 2 0 2 111 2 0 3 0 2 112 3 1 3 0 2 113 2.5 0 4 0 2 114 3 0 5 0 2 115 1.83333 1 0 1 2 116 2.83333 2 0 1 2 117 1.33333 0 1 1 2 118 2.33333 1 1 1 2 119 1.83333 0 2 1 2 120 2.83333 1 2 1 2 121 2.33333 0 3 1 2 122 2.83333 0 4 1 2 123 1.16667 0 0 2 2 124 2.16667 1 0 2 2 125 1.66667 0 1 2 2 126 2.66667 1 1 2 2 127 2.16667 0 2 2 2 128 2.66667 0 3 2 2 129 1.5 0 0 3 2 130 2.5 1 0 3 2 131 2 0 1 3 2 132 3 1 1 3 2 133 2.5 0 2 3 2 134 3 0 3 3 2 135 1.83333 0 0 4 2 136 2.83333 1 0 4 2 137 2.33333 0 1 4 2 138 2.83333 0 2 4 2 139 2.16667 0 0 5 2 140 2.66667 0 1 5 2 141 2.5 0 0 6 2 142 3 0 1 6 2 143 2.83333 0 0 7 2 144 1.75 1 0 0 3 145 2.75 2 0 0 3 146 1.25 0 1 0 3 147 2.25 1 1 0 3 148 1.75 0 2 0 3 149 2.75 1 2 0 3 150 2.25 0 3 0 3 151 2.75 0 4 0 3 152 1.08333 0 0 1 3 153 2.08333 1 0 1 3 154 1.58333 0 1 1 3 155 2.58333 1 1 1 3 156 2.08333 0 2 1 3 157 2.58333 0 3 1 3 158 1.41667 0 0 2 3 159 2.41667 1 0 2 3 160 1.91667 0 1 2 3 161 2.91667 1 1 2 3 162 2.41667 0 2 2 3 163 2.91667 0 3 2 3 164 1.75 0 0 3 3 165 2.75 1 0 3 3 166 2.25 0 1 3 3 167 2.75 0 2 3 3 168 2.08333 0 0 4 3 169 2.58333 0 1 4 3 170 2.41667 0 0 5 3 171 2.91667 0 1 5 3 172 2.75 0 0 6 3 173 1 0 0 0 4 174 2 1 0 0 4 175 3 2 0 0 4 176 1.5 0 1 0 4 177 2.5 1 1 0 4 178 2 0 2 0 4 179 3 1 2 0 4 180 2.5 0 3 0 4 181 3 0 4 0 4 182 1.33333 0 0 1 4 183 2.33333 1 0 1 4 184 1.83333 0 1 1 4 185 2.83333 1 1 1 4 186 2.33333 0 2 1 4 187 2.83333 0 3 1 4 188 1.66667 0 0 2 4 189 2.66667 1 0 2 4 190 2.16667 0 1 2 4 191 2.66667 0 2 2 4 192 2 0 0 3 4 193 3 1 0 3 4 194 2.5 0 1 3 4 195 3 0 2 3 4 196 2.33333 0 0 4 4 197 2.83333 0 1 4 4 198 2.66667 0 0 5 4 199 3 0 0 6 4 200 1.25 0 0 0 5 201 2.25 1 0 0 5 202 1.75 0 1 0 5 203 2.75 1 1 0 5 204 2.25 0 2 0 5 205 2.75 0 3 0 5 206 1.58333 0 0 1 5 207 2.58333 1 0 1 5 208 2.08333 0 1 1 5 209 2.58333 0 2 1 5 210 1.91667 0 0 2 5 211 2.91667 1 0 2 5 212 2.41667 0 1 2 5 213 2.91667 0 2 2 5 214 2.25 0 0 3 5 215 2.75 0 1 3 5 216 2.58333 0 0 4 5 217 2.91667 0 0 5 5 218 1.5 0 0 0 6 219 2.5 1 0 0 6 220 2 0 1 0 6 221 3 1 1 0 6 222 2.5 0 2 0 6 223 3 0 3 0 6 224 1.83333 0 0 1 6 225 2.83333 1 0 1 6 226 2.33333 0 1 1 6 227 2.83333 0 2 1 6 228 2.16667 0 0 2 6 229 2.66667 0 1 2 6 230 2.5 0 0 3 6 231 3 0 1 3 6 232 2.83333 0 0 4 6 233 1.75 0 0 0 7 234 2.75 1 0 0 7 235 2.25 0 1 0 7 236 2.75 0 2 0 7 237 2.08333 0 0 1 7 238 2.58333 0 1 1 7 239 2.41667 0 0 2 7 240 2.91667 0 1 2 7 241 2.75 0 0 3 7 242 2 0 0 0 8 243 3 1 0 0 8 244 2.5 0 1 0 8 245 3 0 2 0 8 246 2.33333 0 0 1 8 247 2.83333 0 1 1 8 248 2.66667 0 0 2 8 249 3 0 0 3 8 250 2.25 0 0 0 9 251 2.75 0 1 0 9 252 2.58333 0 0 1 9 253 2.91667 0 0 2 9 254 2.5 0 0 0 10 255 3 0 1 0 10 256 2.83333 0 0 1 10 257 2.75 0 0 0 11 258 3 0 0 0 12 MAX 3 3 6 9 12 SGMGA_VCN I Q X MIN 0.916667 0 0 0 0 1 1 1 0 0 0 2 2 2 0 0 0 3 3 3 0 0 0 4 1.5 1 1 0 0 5 2.5 2 1 0 0 6 1 0 2 0 0 7 2 1 2 0 0 8 3 2 2 0 0 9 1.5 0 3 0 0 10 2.5 1 3 0 0 11 2 0 4 0 0 12 3 1 4 0 0 13 2.5 0 5 0 0 14 3 0 6 0 0 15 1.33333 1 0 1 0 16 2.33333 2 0 1 0 17 1.83333 1 1 1 0 18 2.83333 2 1 1 0 19 1.33333 0 2 1 0 20 2.33333 1 2 1 0 21 1.83333 0 3 1 0 22 2.83333 1 3 1 0 23 2.33333 0 4 1 0 24 2.83333 0 5 1 0 25 1.66667 1 0 2 0 26 2.66667 2 0 2 0 27 1.16667 0 1 2 0 28 2.16667 1 1 2 0 29 1.66667 0 2 2 0 30 2.66667 1 2 2 0 31 2.16667 0 3 2 0 32 2.66667 0 4 2 0 33 1 0 0 3 0 34 2 1 0 3 0 35 3 2 0 3 0 36 1.5 0 1 3 0 37 2.5 1 1 3 0 38 2 0 2 3 0 39 3 1 2 3 0 40 2.5 0 3 3 0 41 3 0 4 3 0 42 1.33333 0 0 4 0 43 2.33333 1 0 4 0 44 1.83333 0 1 4 0 45 2.83333 1 1 4 0 46 2.33333 0 2 4 0 47 2.83333 0 3 4 0 48 1.66667 0 0 5 0 49 2.66667 1 0 5 0 50 2.16667 0 1 5 0 51 2.66667 0 2 5 0 52 2 0 0 6 0 53 3 1 0 6 0 54 2.5 0 1 6 0 55 3 0 2 6 0 56 2.33333 0 0 7 0 57 2.83333 0 1 7 0 58 2.66667 0 0 8 0 59 3 0 0 9 0 60 1.25 1 0 0 1 61 2.25 2 0 0 1 62 1.75 1 1 0 1 63 2.75 2 1 0 1 64 1.25 0 2 0 1 65 2.25 1 2 0 1 66 1.75 0 3 0 1 67 2.75 1 3 0 1 68 2.25 0 4 0 1 69 2.75 0 5 0 1 70 1.58333 1 0 1 1 71 2.58333 2 0 1 1 72 1.08333 0 1 1 1 73 2.08333 1 1 1 1 74 1.58333 0 2 1 1 75 2.58333 1 2 1 1 76 2.08333 0 3 1 1 77 2.58333 0 4 1 1 78 1.91667 1 0 2 1 79 2.91667 2 0 2 1 80 1.41667 0 1 2 1 81 2.41667 1 1 2 1 82 1.91667 0 2 2 1 83 2.91667 1 2 2 1 84 2.41667 0 3 2 1 85 2.91667 0 4 2 1 86 1.25 0 0 3 1 87 2.25 1 0 3 1 88 1.75 0 1 3 1 89 2.75 1 1 3 1 90 2.25 0 2 3 1 91 2.75 0 3 3 1 92 1.58333 0 0 4 1 93 2.58333 1 0 4 1 94 2.08333 0 1 4 1 95 2.58333 0 2 4 1 96 1.91667 0 0 5 1 97 2.91667 1 0 5 1 98 2.41667 0 1 5 1 99 2.91667 0 2 5 1 100 2.25 0 0 6 1 101 2.75 0 1 6 1 102 2.58333 0 0 7 1 103 2.91667 0 0 8 1 104 1.5 1 0 0 2 105 2.5 2 0 0 2 106 1 0 1 0 2 107 2 1 1 0 2 108 3 2 1 0 2 109 1.5 0 2 0 2 110 2.5 1 2 0 2 111 2 0 3 0 2 112 3 1 3 0 2 113 2.5 0 4 0 2 114 3 0 5 0 2 115 1.83333 1 0 1 2 116 2.83333 2 0 1 2 117 1.33333 0 1 1 2 118 2.33333 1 1 1 2 119 1.83333 0 2 1 2 120 2.83333 1 2 1 2 121 2.33333 0 3 1 2 122 2.83333 0 4 1 2 123 1.16667 0 0 2 2 124 2.16667 1 0 2 2 125 1.66667 0 1 2 2 126 2.66667 1 1 2 2 127 2.16667 0 2 2 2 128 2.66667 0 3 2 2 129 1.5 0 0 3 2 130 2.5 1 0 3 2 131 2 0 1 3 2 132 3 1 1 3 2 133 2.5 0 2 3 2 134 3 0 3 3 2 135 1.83333 0 0 4 2 136 2.83333 1 0 4 2 137 2.33333 0 1 4 2 138 2.83333 0 2 4 2 139 2.16667 0 0 5 2 140 2.66667 0 1 5 2 141 2.5 0 0 6 2 142 3 0 1 6 2 143 2.83333 0 0 7 2 144 1.75 1 0 0 3 145 2.75 2 0 0 3 146 1.25 0 1 0 3 147 2.25 1 1 0 3 148 1.75 0 2 0 3 149 2.75 1 2 0 3 150 2.25 0 3 0 3 151 2.75 0 4 0 3 152 1.08333 0 0 1 3 153 2.08333 1 0 1 3 154 1.58333 0 1 1 3 155 2.58333 1 1 1 3 156 2.08333 0 2 1 3 157 2.58333 0 3 1 3 158 1.41667 0 0 2 3 159 2.41667 1 0 2 3 160 1.91667 0 1 2 3 161 2.91667 1 1 2 3 162 2.41667 0 2 2 3 163 2.91667 0 3 2 3 164 1.75 0 0 3 3 165 2.75 1 0 3 3 166 2.25 0 1 3 3 167 2.75 0 2 3 3 168 2.08333 0 0 4 3 169 2.58333 0 1 4 3 170 2.41667 0 0 5 3 171 2.91667 0 1 5 3 172 2.75 0 0 6 3 173 1 0 0 0 4 174 2 1 0 0 4 175 3 2 0 0 4 176 1.5 0 1 0 4 177 2.5 1 1 0 4 178 2 0 2 0 4 179 3 1 2 0 4 180 2.5 0 3 0 4 181 3 0 4 0 4 182 1.33333 0 0 1 4 183 2.33333 1 0 1 4 184 1.83333 0 1 1 4 185 2.83333 1 1 1 4 186 2.33333 0 2 1 4 187 2.83333 0 3 1 4 188 1.66667 0 0 2 4 189 2.66667 1 0 2 4 190 2.16667 0 1 2 4 191 2.66667 0 2 2 4 192 2 0 0 3 4 193 3 1 0 3 4 194 2.5 0 1 3 4 195 3 0 2 3 4 196 2.33333 0 0 4 4 197 2.83333 0 1 4 4 198 2.66667 0 0 5 4 199 3 0 0 6 4 200 1.25 0 0 0 5 201 2.25 1 0 0 5 202 1.75 0 1 0 5 203 2.75 1 1 0 5 204 2.25 0 2 0 5 205 2.75 0 3 0 5 206 1.58333 0 0 1 5 207 2.58333 1 0 1 5 208 2.08333 0 1 1 5 209 2.58333 0 2 1 5 210 1.91667 0 0 2 5 211 2.91667 1 0 2 5 212 2.41667 0 1 2 5 213 2.91667 0 2 2 5 214 2.25 0 0 3 5 215 2.75 0 1 3 5 216 2.58333 0 0 4 5 217 2.91667 0 0 5 5 218 1.5 0 0 0 6 219 2.5 1 0 0 6 220 2 0 1 0 6 221 3 1 1 0 6 222 2.5 0 2 0 6 223 3 0 3 0 6 224 1.83333 0 0 1 6 225 2.83333 1 0 1 6 226 2.33333 0 1 1 6 227 2.83333 0 2 1 6 228 2.16667 0 0 2 6 229 2.66667 0 1 2 6 230 2.5 0 0 3 6 231 3 0 1 3 6 232 2.83333 0 0 4 6 233 1.75 0 0 0 7 234 2.75 1 0 0 7 235 2.25 0 1 0 7 236 2.75 0 2 0 7 237 2.08333 0 0 1 7 238 2.58333 0 1 1 7 239 2.41667 0 0 2 7 240 2.91667 0 1 2 7 241 2.75 0 0 3 7 242 2 0 0 0 8 243 3 1 0 0 8 244 2.5 0 1 0 8 245 3 0 2 0 8 246 2.33333 0 0 1 8 247 2.83333 0 1 1 8 248 2.66667 0 0 2 8 249 3 0 0 3 8 250 2.25 0 0 0 9 251 2.75 0 1 0 9 252 2.58333 0 0 1 9 253 2.91667 0 0 2 9 254 2.5 0 0 0 10 255 3 0 1 0 10 256 2.83333 0 0 1 10 257 2.75 0 0 0 11 258 3 0 0 0 12 MAX 3 3 6 9 12 SGMGA_VCN_TIMING_TESTS calls SGMGA_VCN_TIMING_TEST. SGMGA_VCN_TIMING_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we compare the timings. IMPORTANCE: 1 1 1 1 LEVEL_WEIGHT: 1 1 1 1 SGMGA_VCN_NAIVE I Q X MIN -2 0 0 0 0 MAX 2 2 2 2 2 TIME 3e-06 SGMGA_VCN I Q X MIN -2 0 0 0 0 MAX 2 2 2 2 2 TIME 2e-06 SGMGA_VCN_TIMING_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we compare the timings. IMPORTANCE: 1 1 1 1 1 1 1 1 LEVEL_WEIGHT: 1 1 1 1 1 1 1 1 SGMGA_VCN_NAIVE I Q X MIN -6 0 0 0 0 0 0 0 0 MAX 2 2 2 2 2 2 2 2 2 TIME 0.000174 SGMGA_VCN I Q X MIN -6 0 0 0 0 0 0 0 0 MAX 2 2 2 2 2 2 2 2 2 TIME 7e-06 SGMGA_VCN_TIMING_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we compare the timings. IMPORTANCE: 1 2 3 4 LEVEL_WEIGHT: 1 0.5 0.333333 0.25 SGMGA_VCN_NAIVE I Q X MIN -0.0833333 0 0 0 0 MAX 2 2 4 6 8 TIME 1.5e-05 SGMGA_VCN I Q X MIN -0.0833333 0 0 0 0 MAX 2 2 4 6 8 TIME 7e-06 SGMGA_VCN_TIMING_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept vectors for which Q_MIN < Q <= Q_MAX No particular order is imposed on the LEVEL_1D values. SGMGA_VCN_NAIVE uses a naive approach; SGMGA_VCN tries to be more efficient. Here, we compare the timings. IMPORTANCE: 1 2 3 4 5 6 7 8 LEVEL_WEIGHT: 1 0.5 0.333333 0.25 0.2 0.166667 0.142857 0.125 SGMGA_VCN_NAIVE I Q X MIN -0.717857 0 0 0 0 0 0 0 0 MAX 2 2 4 6 8 10 12 14 16 TIME 0.759973 SGMGA_VCN I Q X MIN -0.717857 0 0 0 0 0 0 0 0 MAX 2 2 4 6 8 10 12 14 16 TIME 0.001571 SGMGA_VCN_ORDERED_TESTS calls SGMGA_VCN_ORDERED_TEST. SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 1 LEVEL_WEIGHT: 1 1 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN -2 0 0 1 0 0 0 MAX 0 1 1 SGMGA_VCN_ORDERED: I Q X MIN -2 0 0 1 0 0 0 MAX 0 1 1 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 1 LEVEL_WEIGHT: 1 1 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN -1 0 0 1 0 0 0 2 1 1 0 3 1 0 1 MAX 1 2 2 SGMGA_VCN_ORDERED: I Q X MIN -1 0 0 1 0 0 0 2 1 1 0 3 1 0 1 MAX 1 2 2 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 1 LEVEL_WEIGHT: 1 1 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN 0 0 0 1 1 1 0 2 1 0 1 3 2 2 0 4 2 1 1 5 2 0 2 MAX 2 3 3 SGMGA_VCN_ORDERED: I Q X MIN 0 0 0 1 1 1 0 2 1 0 1 3 2 2 0 4 2 1 1 5 2 0 2 MAX 2 3 3 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 1 LEVEL_WEIGHT: 1 1 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN 1 0 0 1 2 2 0 2 2 1 1 3 2 0 2 4 3 3 0 5 3 2 1 6 3 1 2 7 3 0 3 MAX 3 4 4 SGMGA_VCN_ORDERED: I Q X MIN 1 0 0 1 2 2 0 2 2 1 1 3 2 0 2 4 3 3 0 5 3 2 1 6 3 1 2 7 3 0 3 MAX 3 4 4 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 1 LEVEL_WEIGHT: 1 1 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN 2 0 0 1 3 3 0 2 3 2 1 3 3 1 2 4 3 0 3 5 4 4 0 6 4 3 1 7 4 2 2 8 4 1 3 9 4 0 4 MAX 4 5 5 SGMGA_VCN_ORDERED: I Q X MIN 2 0 0 1 3 3 0 2 3 2 1 3 3 1 2 4 3 0 3 5 4 4 0 6 4 3 1 7 4 2 2 8 4 1 3 9 4 0 4 MAX 4 5 5 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 1 1 LEVEL_WEIGHT: 1 1 1 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN -3 0 0 0 1 0 0 0 0 MAX 0 1 1 1 SGMGA_VCN_ORDERED: I Q X MIN -3 0 0 0 1 0 0 0 0 MAX 0 1 1 1 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 1 1 LEVEL_WEIGHT: 1 1 1 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN -2 0 0 0 1 0 0 0 0 2 1 1 0 0 3 1 0 1 0 4 1 0 0 1 MAX 1 2 2 2 SGMGA_VCN_ORDERED: I Q X MIN -2 0 0 0 1 0 0 0 0 2 1 1 0 0 3 1 0 1 0 4 1 0 0 1 MAX 1 2 2 2 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 1 1 LEVEL_WEIGHT: 1 1 1 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN -1 0 0 0 1 0 0 0 0 2 1 1 0 0 3 1 0 1 0 4 1 0 0 1 5 2 2 0 0 6 2 1 1 0 7 2 0 2 0 8 2 1 0 1 9 2 0 1 1 10 2 0 0 2 MAX 2 3 3 3 SGMGA_VCN_ORDERED: I Q X MIN -1 0 0 0 1 0 0 0 0 2 1 1 0 0 3 1 0 1 0 4 1 0 0 1 5 2 2 0 0 6 2 1 1 0 7 2 0 2 0 8 2 1 0 1 9 2 0 1 1 10 2 0 0 2 MAX 2 3 3 3 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 1 1 LEVEL_WEIGHT: 1 1 1 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN 0 0 0 0 1 1 1 0 0 2 1 0 1 0 3 1 0 0 1 4 2 2 0 0 5 2 1 1 0 6 2 0 2 0 7 2 1 0 1 8 2 0 1 1 9 2 0 0 2 10 3 3 0 0 11 3 2 1 0 12 3 1 2 0 13 3 0 3 0 14 3 2 0 1 15 3 1 1 1 16 3 0 2 1 17 3 1 0 2 18 3 0 1 2 19 3 0 0 3 MAX 3 4 4 4 SGMGA_VCN_ORDERED: I Q X MIN 0 0 0 0 1 1 1 0 0 2 1 0 1 0 3 1 0 0 1 4 2 2 0 0 5 2 1 1 0 6 2 0 2 0 7 2 1 0 1 8 2 0 1 1 9 2 0 0 2 10 3 3 0 0 11 3 2 1 0 12 3 1 2 0 13 3 0 3 0 14 3 2 0 1 15 3 1 1 1 16 3 0 2 1 17 3 1 0 2 18 3 0 1 2 19 3 0 0 3 MAX 3 4 4 4 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 1 1 LEVEL_WEIGHT: 1 1 1 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN 1 0 0 0 1 2 2 0 0 2 2 1 1 0 3 2 0 2 0 4 2 1 0 1 5 2 0 1 1 6 2 0 0 2 7 3 3 0 0 8 3 2 1 0 9 3 1 2 0 10 3 0 3 0 11 3 2 0 1 12 3 1 1 1 13 3 0 2 1 14 3 1 0 2 15 3 0 1 2 16 3 0 0 3 17 4 4 0 0 18 4 3 1 0 19 4 2 2 0 20 4 1 3 0 21 4 0 4 0 22 4 3 0 1 23 4 2 1 1 24 4 1 2 1 25 4 0 3 1 26 4 2 0 2 27 4 1 1 2 28 4 0 2 2 29 4 1 0 3 30 4 0 1 3 31 4 0 0 4 MAX 4 5 5 5 SGMGA_VCN_ORDERED: I Q X MIN 1 0 0 0 1 2 2 0 0 2 2 1 1 0 3 2 0 2 0 4 2 1 0 1 5 2 0 1 1 6 2 0 0 2 7 3 3 0 0 8 3 2 1 0 9 3 1 2 0 10 3 0 3 0 11 3 2 0 1 12 3 1 1 1 13 3 0 2 1 14 3 1 0 2 15 3 0 1 2 16 3 0 0 3 17 4 4 0 0 18 4 3 1 0 19 4 2 2 0 20 4 1 3 0 21 4 0 4 0 22 4 3 0 1 23 4 2 1 1 24 4 1 2 1 25 4 0 3 1 26 4 2 0 2 27 4 1 1 2 28 4 0 2 2 29 4 1 0 3 30 4 0 1 3 31 4 0 0 4 MAX 4 5 5 5 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 1 1 1 LEVEL_WEIGHT: 1 1 1 1 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN -2 0 0 0 0 1 0 0 0 0 0 2 1 1 0 0 0 3 1 0 1 0 0 4 1 0 0 1 0 5 1 0 0 0 1 6 2 2 0 0 0 7 2 1 1 0 0 8 2 0 2 0 0 9 2 1 0 1 0 10 2 0 1 1 0 11 2 0 0 2 0 12 2 1 0 0 1 13 2 0 1 0 1 14 2 0 0 1 1 15 2 0 0 0 2 MAX 2 3 3 3 3 SGMGA_VCN_ORDERED: I Q X MIN -2 0 0 0 0 1 0 0 0 0 0 2 1 1 0 0 0 3 1 0 1 0 0 4 1 0 0 1 0 5 1 0 0 0 1 6 2 2 0 0 0 7 2 1 1 0 0 8 2 0 2 0 0 9 2 1 0 1 0 10 2 0 1 1 0 11 2 0 0 2 0 12 2 1 0 0 1 13 2 0 1 0 1 14 2 0 0 1 1 15 2 0 0 0 2 MAX 2 3 3 3 3 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 1 1 1 LEVEL_WEIGHT: 1 1 1 1 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN -1 0 0 0 0 1 0 0 0 0 0 2 1 1 0 0 0 3 1 0 1 0 0 4 1 0 0 1 0 5 1 0 0 0 1 6 2 2 0 0 0 7 2 1 1 0 0 8 2 0 2 0 0 9 2 1 0 1 0 10 2 0 1 1 0 11 2 0 0 2 0 12 2 1 0 0 1 13 2 0 1 0 1 14 2 0 0 1 1 15 2 0 0 0 2 16 3 3 0 0 0 17 3 2 1 0 0 18 3 1 2 0 0 19 3 0 3 0 0 20 3 2 0 1 0 21 3 1 1 1 0 22 3 0 2 1 0 23 3 1 0 2 0 24 3 0 1 2 0 25 3 0 0 3 0 26 3 2 0 0 1 27 3 1 1 0 1 28 3 0 2 0 1 29 3 1 0 1 1 30 3 0 1 1 1 31 3 0 0 2 1 32 3 1 0 0 2 33 3 0 1 0 2 34 3 0 0 1 2 35 3 0 0 0 3 MAX 3 4 4 4 4 SGMGA_VCN_ORDERED: I Q X MIN -1 0 0 0 0 1 0 0 0 0 0 2 1 1 0 0 0 3 1 0 1 0 0 4 1 0 0 1 0 5 1 0 0 0 1 6 2 2 0 0 0 7 2 1 1 0 0 8 2 0 2 0 0 9 2 1 0 1 0 10 2 0 1 1 0 11 2 0 0 2 0 12 2 1 0 0 1 13 2 0 1 0 1 14 2 0 0 1 1 15 2 0 0 0 2 16 3 3 0 0 0 17 3 2 1 0 0 18 3 1 2 0 0 19 3 0 3 0 0 20 3 2 0 1 0 21 3 1 1 1 0 22 3 0 2 1 0 23 3 1 0 2 0 24 3 0 1 2 0 25 3 0 0 3 0 26 3 2 0 0 1 27 3 1 1 0 1 28 3 0 2 0 1 29 3 1 0 1 1 30 3 0 1 1 1 31 3 0 0 2 1 32 3 1 0 0 2 33 3 0 1 0 2 34 3 0 0 1 2 35 3 0 0 0 3 MAX 3 4 4 4 4 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 2 LEVEL_WEIGHT: 1 0.5 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN -1.5 0 0 1 0 0 0 MAX 0 1 1 SGMGA_VCN_ORDERED: I Q X MIN -1.5 0 0 1 0 0 0 MAX 0 1 1 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 2 LEVEL_WEIGHT: 1 0.5 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN -0.5 0 0 1 0 0 0 2 0.5 0 1 3 1 1 0 4 1 0 2 MAX 1 2 3 SGMGA_VCN_ORDERED: I Q X MIN -0.5 0 0 1 0 0 0 2 0.5 0 1 3 1 1 0 4 1 0 2 MAX 1 2 3 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 2 LEVEL_WEIGHT: 1 0.5 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN 0.5 0 0 1 1 1 0 2 1.5 1 1 3 1 0 2 4 1.5 0 3 5 2 2 0 6 2 1 2 7 2 0 4 MAX 2 3 5 SGMGA_VCN_ORDERED: I Q X MIN 0.5 0 0 1 1 1 0 2 1.5 1 1 3 1 0 2 4 1.5 0 3 5 2 2 0 6 2 1 2 7 2 0 4 MAX 2 3 5 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 2 LEVEL_WEIGHT: 1 0.5 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN 1.5 0 0 1 2 2 0 2 2.5 2 1 3 2 1 2 4 2.5 1 3 5 2 0 4 6 2.5 0 5 7 3 3 0 8 3 2 2 9 3 1 4 10 3 0 6 MAX 3 4 7 SGMGA_VCN_ORDERED: I Q X MIN 1.5 0 0 1 2 2 0 2 2.5 2 1 3 2 1 2 4 2.5 1 3 5 2 0 4 6 2.5 0 5 7 3 3 0 8 3 2 2 9 3 1 4 10 3 0 6 MAX 3 4 7 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 2 LEVEL_WEIGHT: 1 0.5 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN 2.5 0 0 1 3 3 0 2 3.5 3 1 3 3 2 2 4 3.5 2 3 5 3 1 4 6 3.5 1 5 7 3 0 6 8 3.5 0 7 9 4 4 0 10 4 3 2 11 4 2 4 12 4 1 6 13 4 0 8 MAX 4 5 9 SGMGA_VCN_ORDERED: I Q X MIN 2.5 0 0 1 3 3 0 2 3.5 3 1 3 3 2 2 4 3.5 2 3 5 3 1 4 6 3.5 1 5 7 3 0 6 8 3.5 0 7 9 4 4 0 10 4 3 2 11 4 2 4 12 4 1 6 13 4 0 8 MAX 4 5 9 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 2 3 LEVEL_WEIGHT: 1 0.5 0.333333 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN -1.83333 0 0 0 1 0 0 0 0 MAX 0 1 1 1 SGMGA_VCN_ORDERED: I Q X MIN -1.83333 0 0 0 1 0 0 0 0 MAX 0 1 1 1 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 2 3 LEVEL_WEIGHT: 1 0.5 0.333333 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN -0.833333 0 0 0 1 0 0 0 0 2 1 1 0 0 3 0.5 0 1 0 4 1 0 2 0 5 0.333333 0 0 1 6 0.833333 0 1 1 7 0.666667 0 0 2 8 1 0 0 3 MAX 1 2 3 4 SGMGA_VCN_ORDERED: I Q X MIN -0.833333 0 0 0 1 0 0 0 0 2 1 1 0 0 3 0.5 0 1 0 4 1 0 2 0 5 0.333333 0 0 1 6 0.833333 0 1 1 7 0.666667 0 0 2 8 1 0 0 3 MAX 1 2 3 4 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 2 3 LEVEL_WEIGHT: 1 0.5 0.333333 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN 0.166667 0 0 0 1 1 1 0 0 2 0.5 0 1 0 3 1 0 2 0 4 0.333333 0 0 1 5 0.833333 0 1 1 6 0.666667 0 0 2 7 1.16667 0 1 2 8 1 0 0 3 9 2 2 0 0 10 1.5 1 1 0 11 2 1 2 0 12 1.5 0 3 0 13 2 0 4 0 14 1.33333 1 0 1 15 1.83333 1 1 1 16 1.33333 0 2 1 17 1.83333 0 3 1 18 1.66667 1 0 2 19 1.66667 0 2 2 20 2 1 0 3 21 1.5 0 1 3 22 2 0 2 3 23 1.33333 0 0 4 24 1.83333 0 1 4 25 1.66667 0 0 5 26 2 0 0 6 MAX 2 3 5 7 SGMGA_VCN_ORDERED: I Q X MIN 0.166667 0 0 0 1 1 1 0 0 2 0.5 0 1 0 3 1 0 2 0 4 0.333333 0 0 1 5 0.833333 0 1 1 6 0.666667 0 0 2 7 1.16667 0 1 2 8 1 0 0 3 9 2 2 0 0 10 1.5 1 1 0 11 2 1 2 0 12 1.5 0 3 0 13 2 0 4 0 14 1.33333 1 0 1 15 1.83333 1 1 1 16 1.33333 0 2 1 17 1.83333 0 3 1 18 1.66667 1 0 2 19 1.66667 0 2 2 20 2 1 0 3 21 1.5 0 1 3 22 2 0 2 3 23 1.33333 0 0 4 24 1.83333 0 1 4 25 1.66667 0 0 5 26 2 0 0 6 MAX 2 3 5 7 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 2 3 LEVEL_WEIGHT: 1 0.5 0.333333 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN 1.16667 0 0 0 1 2 2 0 0 2 1.5 1 1 0 3 2 1 2 0 4 1.5 0 3 0 5 2 0 4 0 6 1.33333 1 0 1 7 1.83333 1 1 1 8 1.33333 0 2 1 9 1.83333 0 3 1 10 1.66667 1 0 2 11 2.16667 1 1 2 12 1.66667 0 2 2 13 2.16667 0 3 2 14 2 1 0 3 15 1.5 0 1 3 16 2 0 2 3 17 1.33333 0 0 4 18 1.83333 0 1 4 19 1.66667 0 0 5 20 2.16667 0 1 5 21 2 0 0 6 22 3 3 0 0 23 2.5 2 1 0 24 3 2 2 0 25 2.5 1 3 0 26 3 1 4 0 27 2.5 0 5 0 28 3 0 6 0 29 2.33333 2 0 1 30 2.83333 2 1 1 31 2.33333 1 2 1 32 2.83333 1 3 1 33 2.33333 0 4 1 34 2.83333 0 5 1 35 2.66667 2 0 2 36 2.66667 1 2 2 37 2.66667 0 4 2 38 3 2 0 3 39 2.5 1 1 3 40 3 1 2 3 41 2.5 0 3 3 42 3 0 4 3 43 2.33333 1 0 4 44 2.83333 1 1 4 45 2.33333 0 2 4 46 2.83333 0 3 4 47 2.66667 1 0 5 48 2.66667 0 2 5 49 3 1 0 6 50 2.5 0 1 6 51 3 0 2 6 52 2.33333 0 0 7 53 2.83333 0 1 7 54 2.66667 0 0 8 55 3 0 0 9 MAX 3 4 7 10 SGMGA_VCN_ORDERED: I Q X MIN 1.16667 0 0 0 1 2 2 0 0 2 1.5 1 1 0 3 2 1 2 0 4 1.5 0 3 0 5 2 0 4 0 6 1.33333 1 0 1 7 1.83333 1 1 1 8 1.33333 0 2 1 9 1.83333 0 3 1 10 1.66667 1 0 2 11 2.16667 1 1 2 12 1.66667 0 2 2 13 2.16667 0 3 2 14 2 1 0 3 15 1.5 0 1 3 16 2 0 2 3 17 1.33333 0 0 4 18 1.83333 0 1 4 19 1.66667 0 0 5 20 2.16667 0 1 5 21 2 0 0 6 22 3 3 0 0 23 2.5 2 1 0 24 3 2 2 0 25 2.5 1 3 0 26 3 1 4 0 27 2.5 0 5 0 28 3 0 6 0 29 2.33333 2 0 1 30 2.83333 2 1 1 31 2.33333 1 2 1 32 2.83333 1 3 1 33 2.33333 0 4 1 34 2.83333 0 5 1 35 2.66667 2 0 2 36 2.66667 1 2 2 37 2.66667 0 4 2 38 3 2 0 3 39 2.5 1 1 3 40 3 1 2 3 41 2.5 0 3 3 42 3 0 4 3 43 2.33333 1 0 4 44 2.83333 1 1 4 45 2.33333 0 2 4 46 2.83333 0 3 4 47 2.66667 1 0 5 48 2.66667 0 2 5 49 3 1 0 6 50 2.5 0 1 6 51 3 0 2 6 52 2.33333 0 0 7 53 2.83333 0 1 7 54 2.66667 0 0 8 55 3 0 0 9 MAX 3 4 7 10 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 2 3 LEVEL_WEIGHT: 1 0.5 0.333333 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN 2.16667 0 0 0 1 3 3 0 0 2 2.5 2 1 0 3 3 2 2 0 4 2.5 1 3 0 5 3 1 4 0 6 2.5 0 5 0 7 3 0 6 0 8 2.33333 2 0 1 9 2.83333 2 1 1 10 2.33333 1 2 1 11 2.83333 1 3 1 12 2.33333 0 4 1 13 2.83333 0 5 1 14 2.66667 2 0 2 15 3.16667 2 1 2 16 2.66667 1 2 2 17 3.16667 1 3 2 18 2.66667 0 4 2 19 3.16667 0 5 2 20 3 2 0 3 21 2.5 1 1 3 22 3 1 2 3 23 2.5 0 3 3 24 3 0 4 3 25 2.33333 1 0 4 26 2.83333 1 1 4 27 2.33333 0 2 4 28 2.83333 0 3 4 29 2.66667 1 0 5 30 3.16667 1 1 5 31 2.66667 0 2 5 32 3.16667 0 3 5 33 3 1 0 6 34 2.5 0 1 6 35 3 0 2 6 36 2.33333 0 0 7 37 2.83333 0 1 7 38 2.66667 0 0 8 39 3.16667 0 1 8 40 3 0 0 9 41 4 4 0 0 42 3.5 3 1 0 43 4 3 2 0 44 3.5 2 3 0 45 4 2 4 0 46 3.5 1 5 0 47 4 1 6 0 48 3.5 0 7 0 49 4 0 8 0 50 3.33333 3 0 1 51 3.83333 3 1 1 52 3.33333 2 2 1 53 3.83333 2 3 1 54 3.33333 1 4 1 55 3.83333 1 5 1 56 3.33333 0 6 1 57 3.83333 0 7 1 58 3.66667 3 0 2 59 3.66667 2 2 2 60 3.66667 1 4 2 61 3.66667 0 6 2 62 4 3 0 3 63 3.5 2 1 3 64 4 2 2 3 65 3.5 1 3 3 66 4 1 4 3 67 3.5 0 5 3 68 4 0 6 3 69 3.33333 2 0 4 70 3.83333 2 1 4 71 3.33333 1 2 4 72 3.83333 1 3 4 73 3.33333 0 4 4 74 3.83333 0 5 4 75 3.66667 2 0 5 76 3.66667 1 2 5 77 3.66667 0 4 5 78 4 2 0 6 79 3.5 1 1 6 80 4 1 2 6 81 3.5 0 3 6 82 4 0 4 6 83 3.33333 1 0 7 84 3.83333 1 1 7 85 3.33333 0 2 7 86 3.83333 0 3 7 87 3.66667 1 0 8 88 3.66667 0 2 8 89 4 1 0 9 90 3.5 0 1 9 91 4 0 2 9 92 3.33333 0 0 10 93 3.83333 0 1 10 94 3.66667 0 0 11 95 4 0 0 12 MAX 4 5 9 13 SGMGA_VCN_ORDERED: I Q X MIN 2.16667 0 0 0 1 3 3 0 0 2 2.5 2 1 0 3 3 2 2 0 4 2.5 1 3 0 5 3 1 4 0 6 2.5 0 5 0 7 3 0 6 0 8 2.33333 2 0 1 9 2.83333 2 1 1 10 2.33333 1 2 1 11 2.83333 1 3 1 12 2.33333 0 4 1 13 2.83333 0 5 1 14 2.66667 2 0 2 15 3.16667 2 1 2 16 2.66667 1 2 2 17 3.16667 1 3 2 18 2.66667 0 4 2 19 3.16667 0 5 2 20 3 2 0 3 21 2.5 1 1 3 22 3 1 2 3 23 2.5 0 3 3 24 3 0 4 3 25 2.33333 1 0 4 26 2.83333 1 1 4 27 2.33333 0 2 4 28 2.83333 0 3 4 29 2.66667 1 0 5 30 3.16667 1 1 5 31 2.66667 0 2 5 32 3.16667 0 3 5 33 3 1 0 6 34 2.5 0 1 6 35 3 0 2 6 36 2.33333 0 0 7 37 2.83333 0 1 7 38 2.66667 0 0 8 39 3.16667 0 1 8 40 3 0 0 9 41 4 4 0 0 42 3.5 3 1 0 43 4 3 2 0 44 3.5 2 3 0 45 4 2 4 0 46 3.5 1 5 0 47 4 1 6 0 48 3.5 0 7 0 49 4 0 8 0 50 3.33333 3 0 1 51 3.83333 3 1 1 52 3.33333 2 2 1 53 3.83333 2 3 1 54 3.33333 1 4 1 55 3.83333 1 5 1 56 3.33333 0 6 1 57 3.83333 0 7 1 58 3.66667 3 0 2 59 3.66667 2 2 2 60 3.66667 1 4 2 61 3.66667 0 6 2 62 4 3 0 3 63 3.5 2 1 3 64 4 2 2 3 65 3.5 1 3 3 66 4 1 4 3 67 3.5 0 5 3 68 4 0 6 3 69 3.33333 2 0 4 70 3.83333 2 1 4 71 3.33333 1 2 4 72 3.83333 1 3 4 73 3.33333 0 4 4 74 3.83333 0 5 4 75 3.66667 2 0 5 76 3.66667 1 2 5 77 3.66667 0 4 5 78 4 2 0 6 79 3.5 1 1 6 80 4 1 2 6 81 3.5 0 3 6 82 4 0 4 6 83 3.33333 1 0 7 84 3.83333 1 1 7 85 3.33333 0 2 7 86 3.83333 0 3 7 87 3.66667 1 0 8 88 3.66667 0 2 8 89 4 1 0 9 90 3.5 0 1 9 91 4 0 2 9 92 3.33333 0 0 10 93 3.83333 0 1 10 94 3.66667 0 0 11 95 4 0 0 12 MAX 4 5 9 13 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 2 3 4 LEVEL_WEIGHT: 1 0.5 0.333333 0.25 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN -0.0833333 0 0 0 0 1 0 0 0 0 0 2 0.5 0 1 0 0 3 0.333333 0 0 1 0 4 0.833333 0 1 1 0 5 0.666667 0 0 2 0 6 0.25 0 0 0 1 7 0.75 0 1 0 1 8 0.583333 0 0 1 1 9 0.916667 0 0 2 1 10 0.5 0 0 0 2 11 0.833333 0 0 1 2 12 0.75 0 0 0 3 13 1 1 0 0 0 14 1.5 1 1 0 0 15 1 0 2 0 0 16 1.5 0 3 0 0 17 1.33333 1 0 1 0 18 1.83333 1 1 1 0 19 1.33333 0 2 1 0 20 1.83333 0 3 1 0 21 1.66667 1 0 2 0 22 1.16667 0 1 2 0 23 1.66667 0 2 2 0 24 1 0 0 3 0 25 1.5 0 1 3 0 26 1.33333 0 0 4 0 27 1.83333 0 1 4 0 28 1.66667 0 0 5 0 29 1.25 1 0 0 1 30 1.75 1 1 0 1 31 1.25 0 2 0 1 32 1.75 0 3 0 1 33 1.58333 1 0 1 1 34 1.08333 0 1 1 1 35 1.58333 0 2 1 1 36 1.91667 1 0 2 1 37 1.41667 0 1 2 1 38 1.91667 0 2 2 1 39 1.25 0 0 3 1 40 1.75 0 1 3 1 41 1.58333 0 0 4 1 42 1.91667 0 0 5 1 43 1.5 1 0 0 2 44 1 0 1 0 2 45 1.5 0 2 0 2 46 1.83333 1 0 1 2 47 1.33333 0 1 1 2 48 1.83333 0 2 1 2 49 1.16667 0 0 2 2 50 1.66667 0 1 2 2 51 1.5 0 0 3 2 52 1.83333 0 0 4 2 53 1.75 1 0 0 3 54 1.25 0 1 0 3 55 1.75 0 2 0 3 56 1.08333 0 0 1 3 57 1.58333 0 1 1 3 58 1.41667 0 0 2 3 59 1.91667 0 1 2 3 60 1.75 0 0 3 3 61 1 0 0 0 4 62 1.5 0 1 0 4 63 1.33333 0 0 1 4 64 1.83333 0 1 1 4 65 1.66667 0 0 2 4 66 1.25 0 0 0 5 67 1.75 0 1 0 5 68 1.58333 0 0 1 5 69 1.91667 0 0 2 5 70 1.5 0 0 0 6 71 1.83333 0 0 1 6 72 1.75 0 0 0 7 73 2 2 0 0 0 74 2 1 2 0 0 75 2 0 4 0 0 76 2 1 0 3 0 77 2 0 2 3 0 78 2 0 0 6 0 79 2 1 1 0 2 80 2 0 3 0 2 81 2 0 1 3 2 82 2 1 0 0 4 83 2 0 2 0 4 84 2 0 0 3 4 85 2 0 1 0 6 86 2 0 0 0 8 MAX 2 3 5 7 9 SGMGA_VCN_ORDERED: I Q X MIN -0.0833333 0 0 0 0 1 0 0 0 0 0 2 0.5 0 1 0 0 3 0.333333 0 0 1 0 4 0.833333 0 1 1 0 5 0.666667 0 0 2 0 6 0.25 0 0 0 1 7 0.75 0 1 0 1 8 0.583333 0 0 1 1 9 0.916667 0 0 2 1 10 0.5 0 0 0 2 11 0.833333 0 0 1 2 12 0.75 0 0 0 3 13 1 1 0 0 0 14 1.5 1 1 0 0 15 1 0 2 0 0 16 1.5 0 3 0 0 17 1.33333 1 0 1 0 18 1.83333 1 1 1 0 19 1.33333 0 2 1 0 20 1.83333 0 3 1 0 21 1.66667 1 0 2 0 22 1.16667 0 1 2 0 23 1.66667 0 2 2 0 24 1 0 0 3 0 25 1.5 0 1 3 0 26 1.33333 0 0 4 0 27 1.83333 0 1 4 0 28 1.66667 0 0 5 0 29 1.25 1 0 0 1 30 1.75 1 1 0 1 31 1.25 0 2 0 1 32 1.75 0 3 0 1 33 1.58333 1 0 1 1 34 1.08333 0 1 1 1 35 1.58333 0 2 1 1 36 1.91667 1 0 2 1 37 1.41667 0 1 2 1 38 1.91667 0 2 2 1 39 1.25 0 0 3 1 40 1.75 0 1 3 1 41 1.58333 0 0 4 1 42 1.91667 0 0 5 1 43 1.5 1 0 0 2 44 1 0 1 0 2 45 1.5 0 2 0 2 46 1.83333 1 0 1 2 47 1.33333 0 1 1 2 48 1.83333 0 2 1 2 49 1.16667 0 0 2 2 50 1.66667 0 1 2 2 51 1.5 0 0 3 2 52 1.83333 0 0 4 2 53 1.75 1 0 0 3 54 1.25 0 1 0 3 55 1.75 0 2 0 3 56 1.08333 0 0 1 3 57 1.58333 0 1 1 3 58 1.41667 0 0 2 3 59 1.91667 0 1 2 3 60 1.75 0 0 3 3 61 1 0 0 0 4 62 1.5 0 1 0 4 63 1.33333 0 0 1 4 64 1.83333 0 1 1 4 65 1.66667 0 0 2 4 66 1.25 0 0 0 5 67 1.75 0 1 0 5 68 1.58333 0 0 1 5 69 1.91667 0 0 2 5 70 1.5 0 0 0 6 71 1.83333 0 0 1 6 72 1.75 0 0 0 7 73 2 2 0 0 0 74 2 1 2 0 0 75 2 0 4 0 0 76 2 1 0 3 0 77 2 0 2 3 0 78 2 0 0 6 0 79 2 1 1 0 2 80 2 0 3 0 2 81 2 0 1 3 2 82 2 1 0 0 4 83 2 0 2 0 4 84 2 0 0 3 4 85 2 0 1 0 6 86 2 0 0 0 8 MAX 2 3 5 7 9 SGMGA_VCN_ORDERED_TEST Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N), Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) ) Accept only vectors for which Q_MIN < Q <= Q_MAX The solutions are weakly ordered by the value of Q. SGMGA_VCN_ORDERED_NAIVE calls SGMGA_VCN_NAIVE; SGMGA_VCN_ORDERED calls SGMGA_VCN. IMPORTANCE: 1 2 3 4 LEVEL_WEIGHT: 1 0.5 0.333333 0.25 SGMGA_VCN_ORDERED_NAIVE: I Q X MIN 0.916667 0 0 0 0 1 1 1 0 0 0 2 1.5 1 1 0 0 3 1 0 2 0 0 4 1.5 0 3 0 0 5 1.33333 1 0 1 0 6 1.83333 1 1 1 0 7 1.33333 0 2 1 0 8 1.83333 0 3 1 0 9 1.66667 1 0 2 0 10 1.16667 0 1 2 0 11 1.66667 0 2 2 0 12 1 0 0 3 0 13 1.5 0 1 3 0 14 1.33333 0 0 4 0 15 1.83333 0 1 4 0 16 1.66667 0 0 5 0 17 1.25 1 0 0 1 18 1.75 1 1 0 1 19 1.25 0 2 0 1 20 1.75 0 3 0 1 21 1.58333 1 0 1 1 22 1.08333 0 1 1 1 23 1.58333 0 2 1 1 24 1.91667 1 0 2 1 25 1.41667 0 1 2 1 26 1.91667 0 2 2 1 27 1.25 0 0 3 1 28 1.75 0 1 3 1 29 1.58333 0 0 4 1 30 1.91667 0 0 5 1 31 1.5 1 0 0 2 32 1 0 1 0 2 33 1.5 0 2 0 2 34 1.83333 1 0 1 2 35 1.33333 0 1 1 2 36 1.83333 0 2 1 2 37 1.16667 0 0 2 2 38 1.66667 0 1 2 2 39 1.5 0 0 3 2 40 1.83333 0 0 4 2 41 1.75 1 0 0 3 42 1.25 0 1 0 3 43 1.75 0 2 0 3 44 1.08333 0 0 1 3 45 1.58333 0 1 1 3 46 1.41667 0 0 2 3 47 1.91667 0 1 2 3 48 1.75 0 0 3 3 49 1 0 0 0 4 50 1.5 0 1 0 4 51 1.33333 0 0 1 4 52 1.83333 0 1 1 4 53 1.66667 0 0 2 4 54 1.25 0 0 0 5 55 1.75 0 1 0 5 56 1.58333 0 0 1 5 57 1.91667 0 0 2 5 58 1.5 0 0 0 6 59 1.83333 0 0 1 6 60 1.75 0 0 0 7 61 2 2 0 0 0 62 2.5 2 1 0 0 63 2 1 2 0 0 64 2.5 1 3 0 0 65 2 0 4 0 0 66 2.5 0 5 0 0 67 2.33333 2 0 1 0 68 2.83333 2 1 1 0 69 2.33333 1 2 1 0 70 2.83333 1 3 1 0 71 2.33333 0 4 1 0 72 2.83333 0 5 1 0 73 2.66667 2 0 2 0 74 2.16667 1 1 2 0 75 2.66667 1 2 2 0 76 2.16667 0 3 2 0 77 2.66667 0 4 2 0 78 2 1 0 3 0 79 2.5 1 1 3 0 80 2 0 2 3 0 81 2.5 0 3 3 0 82 2.33333 1 0 4 0 83 2.83333 1 1 4 0 84 2.33333 0 2 4 0 85 2.83333 0 3 4 0 86 2.66667 1 0 5 0 87 2.16667 0 1 5 0 88 2.66667 0 2 5 0 89 2 0 0 6 0 90 2.5 0 1 6 0 91 2.33333 0 0 7 0 92 2.83333 0 1 7 0 93 2.66667 0 0 8 0 94 2.25 2 0 0 1 95 2.75 2 1 0 1 96 2.25 1 2 0 1 97 2.75 1 3 0 1 98 2.25 0 4 0 1 99 2.75 0 5 0 1 100 2.58333 2 0 1 1 101 2.08333 1 1 1 1 102 2.58333 1 2 1 1 103 2.08333 0 3 1 1 104 2.58333 0 4 1 1 105 2.91667 2 0 2 1 106 2.41667 1 1 2 1 107 2.91667 1 2 2 1 108 2.41667 0 3 2 1 109 2.91667 0 4 2 1 110 2.25 1 0 3 1 111 2.75 1 1 3 1 112 2.25 0 2 3 1 113 2.75 0 3 3 1 114 2.58333 1 0 4 1 115 2.08333 0 1 4 1 116 2.58333 0 2 4 1 117 2.91667 1 0 5 1 118 2.41667 0 1 5 1 119 2.91667 0 2 5 1 120 2.25 0 0 6 1 121 2.75 0 1 6 1 122 2.58333 0 0 7 1 123 2.91667 0 0 8 1 124 2.5 2 0 0 2 125 2 1 1 0 2 126 2.5 1 2 0 2 127 2 0 3 0 2 128 2.5 0 4 0 2 129 2.83333 2 0 1 2 130 2.33333 1 1 1 2 131 2.83333 1 2 1 2 132 2.33333 0 3 1 2 133 2.83333 0 4 1 2 134 2.16667 1 0 2 2 135 2.66667 1 1 2 2 136 2.16667 0 2 2 2 137 2.66667 0 3 2 2 138 2.5 1 0 3 2 139 2 0 1 3 2 140 2.5 0 2 3 2 141 2.83333 1 0 4 2 142 2.33333 0 1 4 2 143 2.83333 0 2 4 2 144 2.16667 0 0 5 2 145 2.66667 0 1 5 2 146 2.5 0 0 6 2 147 2.83333 0 0 7 2 148 2.75 2 0 0 3 149 2.25 1 1 0 3 150 2.75 1 2 0 3 151 2.25 0 3 0 3 152 2.75 0 4 0 3 153 2.08333 1 0 1 3 154 2.58333 1 1 1 3 155 2.08333 0 2 1 3 156 2.58333 0 3 1 3 157 2.41667 1 0 2 3 158 2.91667 1 1 2 3 159 2.41667 0 2 2 3 160 2.91667 0 3 2 3 161 2.75 1 0 3 3 162 2.25 0 1 3 3 163 2.75 0 2 3 3 164 2.08333 0 0 4 3 165 2.58333 0 1 4 3 166 2.41667 0 0 5 3 167 2.91667 0 1 5 3 168 2.75 0 0 6 3 169 2 1 0 0 4 170 2.5 1 1 0 4 171 2 0 2 0 4 172 2.5 0 3 0 4 173 2.33333 1 0 1 4 174 2.83333 1 1 1 4 175 2.33333 0 2 1 4 176 2.83333 0 3 1 4 177 2.66667 1 0 2 4 178 2.16667 0 1 2 4 179 2.66667 0 2 2 4 180 2 0 0 3 4 181 2.5 0 1 3 4 182 2.33333 0 0 4 4 183 2.83333 0 1 4 4 184 2.66667 0 0 5 4 185 2.25 1 0 0 5 186 2.75 1 1 0 5 187 2.25 0 2 0 5 188 2.75 0 3 0 5 189 2.58333 1 0 1 5 190 2.08333 0 1 1 5 191 2.58333 0 2 1 5 192 2.91667 1 0 2 5 193 2.41667 0 1 2 5 194 2.91667 0 2 2 5 195 2.25 0 0 3 5 196 2.75 0 1 3 5 197 2.58333 0 0 4 5 198 2.91667 0 0 5 5 199 2.5 1 0 0 6 200 2 0 1 0 6 201 2.5 0 2 0 6 202 2.83333 1 0 1 6 203 2.33333 0 1 1 6 204 2.83333 0 2 1 6 205 2.16667 0 0 2 6 206 2.66667 0 1 2 6 207 2.5 0 0 3 6 208 2.83333 0 0 4 6 209 2.75 1 0 0 7 210 2.25 0 1 0 7 211 2.75 0 2 0 7 212 2.08333 0 0 1 7 213 2.58333 0 1 1 7 214 2.41667 0 0 2 7 215 2.91667 0 1 2 7 216 2.75 0 0 3 7 217 2 0 0 0 8 218 2.5 0 1 0 8 219 2.33333 0 0 1 8 220 2.83333 0 1 1 8 221 2.66667 0 0 2 8 222 2.25 0 0 0 9 223 2.75 0 1 0 9 224 2.58333 0 0 1 9 225 2.91667 0 0 2 9 226 2.5 0 0 0 10 227 2.83333 0 0 1 10 228 2.75 0 0 0 11 229 3 3 0 0 0 230 3 2 2 0 0 231 3 1 4 0 0 232 3 0 6 0 0 233 3 2 0 3 0 234 3 1 2 3 0 235 3 0 4 3 0 236 3 1 0 6 0 237 3 0 2 6 0 238 3 0 0 9 0 239 3 2 1 0 2 240 3 1 3 0 2 241 3 0 5 0 2 242 3 1 1 3 2 243 3 0 3 3 2 244 3 0 1 6 2 245 3 2 0 0 4 246 3 1 2 0 4 247 3 0 4 0 4 248 3 1 0 3 4 249 3 0 2 3 4 250 3 0 0 6 4 251 3 1 1 0 6 252 3 0 3 0 6 253 3 0 1 3 6 254 3 1 0 0 8 255 3 0 2 0 8 256 3 0 0 3 8 257 3 0 1 0 10 258 3 0 0 0 12 MAX 3 4 7 10 13 SGMGA_VCN_ORDERED: I Q X MIN 0.916667 0 0 0 0 1 1 1 0 0 0 2 1.5 1 1 0 0 3 1 0 2 0 0 4 1.5 0 3 0 0 5 1.33333 1 0 1 0 6 1.83333 1 1 1 0 7 1.33333 0 2 1 0 8 1.83333 0 3 1 0 9 1.66667 1 0 2 0 10 1.16667 0 1 2 0 11 1.66667 0 2 2 0 12 1 0 0 3 0 13 1.5 0 1 3 0 14 1.33333 0 0 4 0 15 1.83333 0 1 4 0 16 1.66667 0 0 5 0 17 1.25 1 0 0 1 18 1.75 1 1 0 1 19 1.25 0 2 0 1 20 1.75 0 3 0 1 21 1.58333 1 0 1 1 22 1.08333 0 1 1 1 23 1.58333 0 2 1 1 24 1.91667 1 0 2 1 25 1.41667 0 1 2 1 26 1.91667 0 2 2 1 27 1.25 0 0 3 1 28 1.75 0 1 3 1 29 1.58333 0 0 4 1 30 1.91667 0 0 5 1 31 1.5 1 0 0 2 32 1 0 1 0 2 33 1.5 0 2 0 2 34 1.83333 1 0 1 2 35 1.33333 0 1 1 2 36 1.83333 0 2 1 2 37 1.16667 0 0 2 2 38 1.66667 0 1 2 2 39 1.5 0 0 3 2 40 1.83333 0 0 4 2 41 1.75 1 0 0 3 42 1.25 0 1 0 3 43 1.75 0 2 0 3 44 1.08333 0 0 1 3 45 1.58333 0 1 1 3 46 1.41667 0 0 2 3 47 1.91667 0 1 2 3 48 1.75 0 0 3 3 49 1 0 0 0 4 50 1.5 0 1 0 4 51 1.33333 0 0 1 4 52 1.83333 0 1 1 4 53 1.66667 0 0 2 4 54 1.25 0 0 0 5 55 1.75 0 1 0 5 56 1.58333 0 0 1 5 57 1.91667 0 0 2 5 58 1.5 0 0 0 6 59 1.83333 0 0 1 6 60 1.75 0 0 0 7 61 2 2 0 0 0 62 2.5 2 1 0 0 63 2 1 2 0 0 64 2.5 1 3 0 0 65 2 0 4 0 0 66 2.5 0 5 0 0 67 2.33333 2 0 1 0 68 2.83333 2 1 1 0 69 2.33333 1 2 1 0 70 2.83333 1 3 1 0 71 2.33333 0 4 1 0 72 2.83333 0 5 1 0 73 2.66667 2 0 2 0 74 2.16667 1 1 2 0 75 2.66667 1 2 2 0 76 2.16667 0 3 2 0 77 2.66667 0 4 2 0 78 2 1 0 3 0 79 2.5 1 1 3 0 80 2 0 2 3 0 81 2.5 0 3 3 0 82 2.33333 1 0 4 0 83 2.83333 1 1 4 0 84 2.33333 0 2 4 0 85 2.83333 0 3 4 0 86 2.66667 1 0 5 0 87 2.16667 0 1 5 0 88 2.66667 0 2 5 0 89 2 0 0 6 0 90 2.5 0 1 6 0 91 2.33333 0 0 7 0 92 2.83333 0 1 7 0 93 2.66667 0 0 8 0 94 2.25 2 0 0 1 95 2.75 2 1 0 1 96 2.25 1 2 0 1 97 2.75 1 3 0 1 98 2.25 0 4 0 1 99 2.75 0 5 0 1 100 2.58333 2 0 1 1 101 2.08333 1 1 1 1 102 2.58333 1 2 1 1 103 2.08333 0 3 1 1 104 2.58333 0 4 1 1 105 2.91667 2 0 2 1 106 2.41667 1 1 2 1 107 2.91667 1 2 2 1 108 2.41667 0 3 2 1 109 2.91667 0 4 2 1 110 2.25 1 0 3 1 111 2.75 1 1 3 1 112 2.25 0 2 3 1 113 2.75 0 3 3 1 114 2.58333 1 0 4 1 115 2.08333 0 1 4 1 116 2.58333 0 2 4 1 117 2.91667 1 0 5 1 118 2.41667 0 1 5 1 119 2.91667 0 2 5 1 120 2.25 0 0 6 1 121 2.75 0 1 6 1 122 2.58333 0 0 7 1 123 2.91667 0 0 8 1 124 2.5 2 0 0 2 125 2 1 1 0 2 126 2.5 1 2 0 2 127 2 0 3 0 2 128 2.5 0 4 0 2 129 2.83333 2 0 1 2 130 2.33333 1 1 1 2 131 2.83333 1 2 1 2 132 2.33333 0 3 1 2 133 2.83333 0 4 1 2 134 2.16667 1 0 2 2 135 2.66667 1 1 2 2 136 2.16667 0 2 2 2 137 2.66667 0 3 2 2 138 2.5 1 0 3 2 139 2 0 1 3 2 140 2.5 0 2 3 2 141 2.83333 1 0 4 2 142 2.33333 0 1 4 2 143 2.83333 0 2 4 2 144 2.16667 0 0 5 2 145 2.66667 0 1 5 2 146 2.5 0 0 6 2 147 2.83333 0 0 7 2 148 2.75 2 0 0 3 149 2.25 1 1 0 3 150 2.75 1 2 0 3 151 2.25 0 3 0 3 152 2.75 0 4 0 3 153 2.08333 1 0 1 3 154 2.58333 1 1 1 3 155 2.08333 0 2 1 3 156 2.58333 0 3 1 3 157 2.41667 1 0 2 3 158 2.91667 1 1 2 3 159 2.41667 0 2 2 3 160 2.91667 0 3 2 3 161 2.75 1 0 3 3 162 2.25 0 1 3 3 163 2.75 0 2 3 3 164 2.08333 0 0 4 3 165 2.58333 0 1 4 3 166 2.41667 0 0 5 3 167 2.91667 0 1 5 3 168 2.75 0 0 6 3 169 2 1 0 0 4 170 2.5 1 1 0 4 171 2 0 2 0 4 172 2.5 0 3 0 4 173 2.33333 1 0 1 4 174 2.83333 1 1 1 4 175 2.33333 0 2 1 4 176 2.83333 0 3 1 4 177 2.66667 1 0 2 4 178 2.16667 0 1 2 4 179 2.66667 0 2 2 4 180 2 0 0 3 4 181 2.5 0 1 3 4 182 2.33333 0 0 4 4 183 2.83333 0 1 4 4 184 2.66667 0 0 5 4 185 2.25 1 0 0 5 186 2.75 1 1 0 5 187 2.25 0 2 0 5 188 2.75 0 3 0 5 189 2.58333 1 0 1 5 190 2.08333 0 1 1 5 191 2.58333 0 2 1 5 192 2.91667 1 0 2 5 193 2.41667 0 1 2 5 194 2.91667 0 2 2 5 195 2.25 0 0 3 5 196 2.75 0 1 3 5 197 2.58333 0 0 4 5 198 2.91667 0 0 5 5 199 2.5 1 0 0 6 200 2 0 1 0 6 201 2.5 0 2 0 6 202 2.83333 1 0 1 6 203 2.33333 0 1 1 6 204 2.83333 0 2 1 6 205 2.16667 0 0 2 6 206 2.66667 0 1 2 6 207 2.5 0 0 3 6 208 2.83333 0 0 4 6 209 2.75 1 0 0 7 210 2.25 0 1 0 7 211 2.75 0 2 0 7 212 2.08333 0 0 1 7 213 2.58333 0 1 1 7 214 2.41667 0 0 2 7 215 2.91667 0 1 2 7 216 2.75 0 0 3 7 217 2 0 0 0 8 218 2.5 0 1 0 8 219 2.33333 0 0 1 8 220 2.83333 0 1 1 8 221 2.66667 0 0 2 8 222 2.25 0 0 0 9 223 2.75 0 1 0 9 224 2.58333 0 0 1 9 225 2.91667 0 0 2 9 226 2.5 0 0 0 10 227 2.83333 0 0 1 10 228 2.75 0 0 0 11 229 3 3 0 0 0 230 3 2 2 0 0 231 3 1 4 0 0 232 3 0 6 0 0 233 3 2 0 3 0 234 3 1 2 3 0 235 3 0 4 3 0 236 3 1 0 6 0 237 3 0 2 6 0 238 3 0 0 9 0 239 3 2 1 0 2 240 3 1 3 0 2 241 3 0 5 0 2 242 3 1 1 3 2 243 3 0 3 3 2 244 3 0 1 6 2 245 3 2 0 0 4 246 3 1 2 0 4 247 3 0 4 0 4 248 3 1 0 3 4 249 3 0 2 3 4 250 3 0 0 6 4 251 3 1 1 0 6 252 3 0 3 0 6 253 3 0 1 3 6 254 3 1 0 0 8 255 3 0 2 0 8 256 3 0 0 3 8 257 3 0 1 0 10 258 3 0 0 0 12 MAX 3 4 7 10 13 SGMGA_VCN_PRB Normal end of execution. 31 October 2024 08:33:36 AM