Eliminate unnecessary operations and choose appropriate data structures to improve algorithmic efficiency. Look for opportunities to remove redundant computations, avoid unnecessary allocations, and leverage built-in methods instead of manual implementations.
Eliminate unnecessary operations and choose appropriate data structures to improve algorithmic efficiency. Look for opportunities to remove redundant computations, avoid unnecessary allocations, and leverage built-in methods instead of manual implementations.
Key areas to focus on:
saturating_sub
when the subtraction cannot underflow:
```rust
// Instead of this when bounds are guaranteed:
let middle_lines = total_lines.saturating_sub(1);// Use this: let middle_lines = total_lines - 1;
2. **Use built-in iterator methods** instead of manual iteration:
```rust
// Instead of manual zip:
for (p1, p2) in iter::zip(f_x, g_x) {
// Use built-in zip:
for (p1, p2) in f_x.zip(g_x) {
// Consider fixed-size alternatives when applicable
4. **Avoid duplicate iterations** by combining operations or using more efficient algorithms:
```rust
// Instead of iterating twice to find byte and char indices:
if let Some(mut index) = self.state.find("://") {
for (char_index, (byte_index, _)) in self.state.char_indices().enumerate() {
if byte_index == index { index = char_index; break; }
}
// Use char_indices directly with appropriate logic
// Use simplified form: 1_000_000_000 / 60_000
6. **Optimize conditional logic** by restructuring boolean expressions for better performance and clarity:
```rust
// Instead of:
if uniq_hyperlinks.contains(&hyperlink) {
return None;
}
uniq_hyperlinks.insert(hyperlink.clone());
Some((cell, hyperlink))
// Use:
if !uniq_hyperlinks.contains(&hyperlink) {
uniq_hyperlinks.insert(hyperlink.clone());
Some((cell, hyperlink))
} else {
None
}
These optimizations improve both performance and code maintainability while reducing the likelihood of algorithmic inefficiencies.
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