Prioritize algorithmic efficiency and avoid unnecessary computational overhead in performance-critical code paths. Look for opportunities to reduce time complexity and eliminate redundant calculations. Key optimization strategies: - Use squared distance instead of actual distance when only comparison is needed: `prefer distanceSq` over `distance()` for performance-critical geometric calculations - Avoid nested loops when a single pass is sufficient: instead of O(rules × workspaces) complexity, optimize to O(rules) by tracking only changed elements - Eliminate redundant calculations by caching or restructuring computation order - Choose appropriate STL algorithms: use `std::erase_if` directly instead of the `std::ranges::remove_if` + `erase` pattern when possible Example from distance calculations: ```cpp // Instead of: if (m_vBeginDragXY.distance(mousePos) <= *PDRAGTHRESHOLD) // Use: if (m_vBeginDragXY.distanceSq(mousePos) <= (*PDRAGTHRESHOLD * *PDRAGTHRESHOLD)) ``` Example from workspace checking: ```cpp // Instead of checking all workspaces against all rules O(n*m): for (auto& workspace : workspaces) { for (auto& rule : rules) { /* check */ } } // Optimize to O(n) by checking only the changed workspace: recheckPersistent(); // Only checks current workspace against rules ``` This approach reduces CPU cycles in frequently called functions and improves overall application responsiveness.