Summary
Other than regular data structures and sorting, as well as searching methods, there are several other problems that arise. This chapter talks about a small subset of those: generating random numbers and solving constraint satisfaction problems.
Random number generation is useful in lots of ways: encryption, gaming, gambling, simulations, data science—all require good random numbers. Good? There are two important types: pseudo-random numbers and "real" random numbers. While the latter has to be taken from the physical world (computers are deterministic), the former can be implemented with the LCG or the Wichmann-Hill generator (which combines LCGs using magic numbers).
Constraint satisfaction problems are problems that find the best combination that conform to a set of constraints. A technique called backtracking builds a state of the current permutation by using recursion to generate all combinations, but tracking back on those that do not satisfy the required constraints. Both the...
