optimize computational efficiency

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:

// Instead of:
if (m_vBeginDragXY.distance(mousePos) <= *PDRAGTHRESHOLD)

// Use:
if (m_vBeginDragXY.distanceSq(mousePos) <= (*PDRAGTHRESHOLD * *PDRAGTHRESHOLD))

Example from workspace checking:

// 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.