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⚡ Performance & Algorithm Optimization

Understanding how algorithms scale in time and memory is critical for writing high-performance software.

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Core Topics

• Time Complexity: Deep dive into Big O notation ($O(1)$, $O(\log n)$, $O(n)$, $O(n^2)$) with real-code examples.
• Space Complexity: Analyzing auxiliary space usage, call stack overhead, and dynamic allocation.
• Algorithm Analysis: Best-case, average-case, and worst-case mathematical analysis.
• Optimization Techniques: Practical loop unrolling, memoization, caching, and data structure selection.
• Benchmarking Methods: Profiling tools and micro-benchmarking techniques across programming languages.