This problem involves finding two min-max criteria filters to generate the highest score sum in a dataset.
I have a dataset, with 3 columns. x, y, score, with over 1 million of rows.
| x | y | score |
|---|---|---|
| 3.6 | 1.2 | -5 |
| 4.2 | 1.2 | -4 |
| 1.2 | 30.2 | 1 |
| 2.9 | 6.8 | 6 |
| 3.1 | 5.8 | 7 |
| 0.1 | 15.8 | 7 |
The data may or may not have a correlation.
I want to find a criteria filter of min/max on x and y that gives me the highest possible sum of scores.
This is how the query would look like in SQL.
SELECT SUM(score)
FROM mytable
WHERE
x > xmin AND x < xmax AND
y > ymin AND y < ymax
What I am looking for is the optimal values for xmin, xmax, ymin och ymax
What kind of optimization method is needed to solve this problem? And exactly how would the implementation look like?
(Preferrably it would be implemented using Java or postgres sql.)
Not so easy. Here is my MIP model:
minimize sum(i, selected(i)*score(i))
x(i) <= xmin + M*(xBetween(i)+xAbove(i))
x(i) >= xmax - M*(xBetween(i)+xBelow(i))
x(i) >= xmin - M*xBelow(i)
x(i) <= xmax + M*xAbove(i)
y(i) <= ymin + M*(yBetween(i)+yAbove(i))
y(i) >= ymax - M*(yBetween(i)+yBelow(i))
y(i) >= ymin - M*yBelow(i)
y(i) <= ymax + M*yAbove(i)
xBetween(i)+xAbove(i)+xBelow(i)=1
yBetween(i)+yAbove(i)+yBelow(i)=1
selected(i) <= xBetween(i)
selected(i) <= yBetween(i)
selected(i) >= xBetween(i)+yBetween(i)-1
xmin <= xmax
ymin <= ymax
xBetween(i),xAbove(i),xBelow(i) ∈ {0,1}
yBetween(i),yAbove(i),yBelow(i) ∈ {0,1}
selected(i) ∈ {0,1}
The constants M are large enough numbers (I used the range of x or y data).
With some random numbers, I got:
---- 37 VARIABLE z.L = 30.940 objective
---- 44 PARAMETER data data + results
x y score x.betw y.betw selected
i1 1.717 6.611 -8.972
i2 8.433 7.558 -9.880
i3 5.504 6.274 -1.975
i4 3.011 2.839 0.398 1.000 1.000 1.000
i5 2.922 0.864 2.578 1.000 1.000 1.000
i6 2.241 1.025 -5.485 1.000
i7 3.498 6.413 -2.078 1.000
i8 8.563 5.453 -4.480 1.000
i9 0.671 0.315 -6.953
i10 5.002 7.924 8.726
i11 9.981 0.728 -1.547 1.000
i12 5.787 1.757 -7.307 1.000
i13 9.911 5.256 -2.279 1.000
i14 7.623 7.502 -2.507
i15 1.307 1.781 -4.630 1.000
i16 6.397 0.341 8.967
i17 1.595 5.851 -6.221 1.000
i18 2.501 6.212 -4.050 1.000
i19 6.689 3.894 -8.509 1.000
i20 4.354 3.587 -1.973 1.000
i21 3.597 2.430 -7.966 1.000
i22 3.514 2.464 -2.322 1.000
i23 1.315 1.305 -3.518 1.000
i24 1.501 9.334 -6.157
i25 5.891 3.799 -7.753 1.000
i26 8.309 7.834 1.931
i27 2.308 3.000 0.229 1.000 1.000 1.000
i28 6.657 1.255 -9.099 1.000
i29 7.759 7.489 5.662
i30 3.037 0.692 8.915 1.000 1.000 1.000
i31 1.105 2.020 1.929 1.000
i32 5.024 0.051 2.147
i33 1.602 2.696 -2.750 1.000
i34 8.725 4.999 1.881 1.000
i35 2.651 1.513 3.597 1.000 1.000 1.000
i36 2.858 1.742 0.132 1.000 1.000 1.000
i37 5.940 3.306 -6.815 1.000
i38 7.227 3.169 3.138 1.000
i39 6.282 3.221 0.478 1.000
i40 4.638 9.640 -7.512
i41 4.133 9.936 9.734
i42 1.177 3.699 -5.438 1.000
i43 3.142 3.729 3.513 1.000 1.000 1.000
i44 0.466 7.720 5.536
i45 3.386 3.967 8.649 1.000 1.000 1.000
i46 1.821 9.131 -5.975
i47 6.457 1.196 -4.057 1.000
i48 5.607 7.355 -6.055
i49 7.700 0.554 -5.073
i50 2.978 5.763 2.930 1.000 1.000 1.000
range(data) 9.516 9.885 19.614
min(solution) 2.241 0.554
max(solution) 3.498 5.851