Optimize performance by reducing the amount of computation performed, especially on data that won't appear in the final result. Apply these strategies: 1) **Filter early**: Filter collections before performing expensive transformations like sorting ```kotlin // Suboptimal: Sorts all elements, then removes private ones functions.sortBy(KmFunction::name) functions.removeIf { it.visibility.isPrivate } // Optimized: Removes private elements first, then sorts only what's needed functions.removeIf { it.visibility.isPrivate } functions.sortBy(KmFunction::name) ``` 2) **Short-circuit common cases**: Add early returns for special cases like empty collections or single elements ```kotlin public fun Array>.flatten(): List { if (isEmpty()) return emptyList() // Early return for common case // Regular processing... } ``` 3) **Cache expensive results**: Use memoization for operations that may be called repeatedly with the same input ```kotlin private val containsCache = mutableMapOf() override fun containsDeclaration(declaration: IrDeclaration): Boolean = containsCache.getOrPut(declaration) { // Expensive computation here } ``` 4) **Optimize for expected usage patterns**: Special-case handle common scenarios based on benchmarks ```kotlin // Optimization for common no-argument functions if (parameters.isEmpty()) { return reflectionCall { caller.call(if (isSuspend) arrayOf(continuationArgument) else emptyArray()) as R } } ``` Always verify your optimizations with benchmarks to ensure they provide measurable benefits in real-world scenarios.