16 October 2011 12:11:39 PM
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 4e-06
SGMGA_VCN
I Q X
MIN -2 0 0 0 0
MAX 2 2 2 2 2
TIME 3e-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 0.000314
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 1.1e-05
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 2.8e-05
SGMGA_VCN
I Q X
MIN -0.0833333 0 0 0 0
MAX 2 2 4 6 8
TIME 1e-05
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 1.56797
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 0.002582
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.
16 October 2011 12:11:41 PM