Trying to solve 6 Number 1 target android game using oracle (sql or pl/sql)).

This article provides the SQL that does this, and also a PL/SQL package containing a pipelined function that applies a slightly different algorithm; the latter is also practical, although it proved less efficient on my test problems.

**Problem Definition**

- The calculation can be expressed as an additive combination of products
- Within each product, each number occurs once with a power between -P and +P, including zero
- All possible products will be considered
- Each element in the combination has a coefficient between -C and +C, including zero
- The combination has a fixed number of elements, E
- The numbers are entered into a table, and a fixed number of them, N, are to be considered
- The queries are to be generic, parametrised by P, C, E, N, and target value T
- Integer division is messy, so I use real numbers and exclude non-integral complete products so that the order doesn’t matter
- (Added 070813:)Optionally, a number can appear in at most one combination, using the BitAnd idea I borrrowed from Stew’s solution

**Test Problems**

I used two test problems.

*Test Problem 1: Brazilian League*

The first …

Note that …

**SQL Solution with Recursive Subquery Factoring**

*SQL*

Note that currently I have retained the fantasy league table and column names, but they could as well be the generic items and categories in place of players and teams: This is a generic solution.

*How It Works*

The solution approach is based on the method used to provide exact solutions for knapsack problems in my earlier article, but with a number of extensions to cater for the new category constraints, and to reduce searching to manageable proportions.

**Results**

*Test Problem 1: Brazilian League*

The pipelined function solved this in 5 seconds, while the SQL solution solved it in 21 seconds. The solutions were identical, as follows:

**Conclusions**

My idea for using recursive subquery factoring to solve combinatorial optimisation problems, such as knapsack problems, described in other articles on my blog, was previously only practical for small problems. The extensions described here render it a practical proposition even for larger problems. It is also relatively simple compared with procedural approaches.

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