Optimize computational complexity

Identify and reduce computational complexity in your code by minimizing redundant operations and simplifying algorithms. Focus especially on code that processes large datasets or is called frequently.

Key practices:

1. Reduce redundant operations, especially in loops and function calls:

   # Inefficient - O(2N+1) function calls
   for obj in objs:
       filter_kwargs = get_filter_kwargs_for_object(obj)
       # ...process...
       filter_kwargs = get_filter_kwargs_for_object(obj)  # Called again!
          
   # Optimized - O(N) function calls
   for obj in objs:
       filter_kwargs = get_filter_kwargs_for_object(obj)
       # ...use filter_kwargs throughout...
   

2. Use appropriate data structures to improve access patterns:

   # Creating a lookup map for faster access
   max_orders_map = {
       tuple(key_values): max_order
       for key_values, max_order in zip(keys, values)
   }
   # Now O(1) lookups instead of repeated O(n) searches
   

3. Simplify complex conditionals without sacrificing functionality:

   # Verbose approach
   if isinstance(rel, ForeignKey) or isinstance(rel, OneToOneField):
       # do something
      
   # Simplified equivalent
   if isinstance(rel, (ForeignKey, OneToOneField)):
       # do something
   

4. Choose algorithms with appropriate complexity for your data size:

   # Less efficient for large datasets (potentially O(n²))
   return min(desired_types, key=lambda t: self.accepted_types.index(t[0]))[1]
      
   # More efficient direct comparison (O(n))
   return max(desired_types, key=lambda t: t[0].quality)[1]
   

Remember that the most important optimizations target the inner loops and frequently called functions. Document the time and space complexity of critical algorithms to help future developers understand performance considerations.