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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.