Consider the example where $n = 4$, bit 0 is fixed at 1, and bit 2 is fixed at 0. I would like to generate all $n$-bit numbers with those bits fixed. Essentially, everything I generate would have the form:


where $*$ represents a bit that can vary. The fixed bits can be in arbitrary positions, and there can be arbitrarily-many of them (anywhere from 1 bit fixed to $n-1$ bits fixed). Ideally, I would prefer these to be generated in lex order; what I'm looking to generate for the example case would be:

$$1000, 1001, 1100, 1101$$

I've only found solutions for doing something like this when specific bits are set to 1 - my question is more general, as it allows bits to be set to 0 or 1. What is a good algorithm (in terms of simplicity and efficiency) to do this for arbitrary $n$?

  • 2
    $\begingroup$ Hardly a question of computer science, but as you can see, excluding the $k$ bits that are fixed, the others just form a binary enumeration of $n-k$ digits. Generate that sequence, insert the fixed bits in the proper positions, and you're done. $\endgroup$ Jan 14, 2017 at 4:07
  • $\begingroup$ If you can do it when bits are fixed to 1, you can probably use the very same algorithm for the general case. $\endgroup$ Jan 14, 2017 at 11:51

2 Answers 2


Practically speaking, here is one thing you could try:

  1. Make a list $A[1],\ldots,A[m]$ of powers of two corresponding to free bits.

  2. Generate a list of all XORs of elements from $A[i]$ (in the correct order), XORed to a mask consisting of the fixed bits.

If you don't care about the order, then an efficient way to generate the list is using a Gray code. Otherwise, you could use a recursive procedure.


In python:

n = 4
or_mask =  0b1000
and_mask = 0b1101
answer_set = set()
for i in range(2**n):
    answer_set.add((i | or_mask) & and_mask)
return sorted(answer_set)
  • 1
    $\begingroup$ This is not so efficient, since it runs in $O(2^n)$ rather than in $O(2^m)$, where $m$ is the number of free bits. More overhead arises from the set data structure. $\endgroup$ Jan 14, 2017 at 11:48
  • $\begingroup$ Python sets are amortized O(1). And while you're right that there's an extra O(2^k) spent, what the solution loses in efficiency, it gains in simplicity, which was also an explicit goal of the OP. It being a complete, working solution doesn't hurt either. I suspect this question should actually be moved to stack overflow, but I don't have the rep to do so. $\endgroup$ Jan 15, 2017 at 5:58

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