Implement memoization and guard clauses to eliminate redundant computations and improve algorithmic performance. Many algorithms can be significantly optimized by caching intermediate results and avoiding unnecessary work.

Key optimization strategies:

Use memoization for recursive functions - Cache results of expensive recursive calls to avoid recalculating the same values multiple times
Add guard clauses for conditional processing - Check if work is needed before performing expensive operations
Choose efficient iteration patterns - Use lazy evaluation techniques like islice instead of creating full intermediate collections
Ensure proper memoization scope - Create memo objects at the appropriate scope to maximize reuse

Example of proper memoization implementation:

# Before: Redundant recursive calculations
def recursive_will_execute(prompt, outputs, node_id):
    # Expensive recursive computation repeated many times
    return calculate_dependencies(prompt, outputs, node_id)

# After: Memoized version with proper scope
memo = {}  # Create memo outside the sorting algorithm
output_node_id = min(to_execute, key=lambda a: len(recursive_will_execute(prompt, outputs, a, memo)))

Example of guard clause optimization:

# Before: Always processes string replacements
def compute_vars(input, image_width, image_height):
    input = input.replace("%width%", str(image_width))
    input = input.replace("%height%", str(image_height))
    return input

# After: Skip processing if no variables present
def compute_vars(input, image_width, image_height):
    if '%' not in input:
        return input  # Guard clause avoids unnecessary work
    input = input.replace("%width%", str(image_width))
    input = input.replace("%height%", str(image_height))
    return input

These optimizations can dramatically improve performance, especially in algorithms that process large datasets or perform repeated calculations.

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