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Choose appropriate data structures and algorithms to optimize computational complexity and performance. Consider the specific use case and access patterns when selecting between different approaches.

Key optimization opportunities:

1. Use efficient data structures for lookups: Replace linear searches with constant-time operations where possible. For example, convert ArrayContains operations on literal arrays to InSet operations for O(1) lookup instead of O(n).

2. Choose appropriate collection types: Use Set[String] with contains() for simple string matching instead of Set[Regex] when regex functionality isn’t needed. This avoids unnecessary regex compilation overhead.

3. Leverage built-in collection methods: Use Array.tabulate(size)(constructor) instead of manual loops for array initialization, and collection.zip(other).toMap for efficient map construction.

4. Consider algorithmic properties: Be aware of algorithm limitations like XOR checksums having issues with duplicate values. Consider alternatives like combining sum + XOR or using hash functions like Fowler–Noll–Vo when order-independence and duplicate-handling are required.

5. Select semantic-appropriate data structures: Use ExpressionSet for deduplicating expressions by semantics rather than object equality when the logical meaning matters more than object identity.

Example transformation:

// Instead of O(n) array search:
case ArrayContains(arrayParam: Literal, col) =>
  // Linear search through array elements

// Use O(1) set lookup:
case ArrayContains(arrayParam: Literal, col) if arrayParam.value != null =>
  InSet(col, arrayParam.value.asInstanceOf[GenericArrayData].array.toSet)

This approach reduces computational complexity and improves performance, especially for frequently executed operations or large datasets.