Algorithmic Complexity & Code-Level Optimization Questions

Reasoning about the time and space complexity of code and applying local optimizations that materially change performance. Covers Big-O and amortized analysis, performance modeling, data-structure selection, hot-loop and allocation reduction, hardware-aware work such as cache locality, vectorization and false sharing, knowing when an algorithmic change beats micro-optimization, and the measurement practice that grounds those calls: flame graphs, hardware counters, and microbenchmarks that survive warmup, noise and statistical scrutiny. Emphasizes performance-aware coding grounded in complexity and measurement rather than premature tuning, scoped to code you can change rather than production incident response or capacity planning.