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Algorithm Atlas

Problem-First Decision Wizard

Find the Right Algorithm

Start from your problem shape, primary engineering objective, and runtime constraints. The Atlas ranks the best-fitting algorithms across its 1,004-entry knowledge base and lets you launch a side-by-side tradeoff comparison in one click.

Common Engineering Scenarios

Load a pre-configured real-world problem or build your own diagnosis below.

1. What problem domain are you working in?

Choose the primary data structure or computational area.

2. What is your primary goal in Pathfinding & Routing?

Select the single objective that best describes the output you need.

3. Which constraints or workload properties apply?

Check any constraints that matter for your environment to refine the ranking.

Boosts algorithms whose concepts, tags, or use cases match your terms.

Diagnostic Recommendations

Top 6 Matches

Pathfinding & Spatial Routing · Shortest path between two points with a distance heuristic

Compare Top 3 Side-by-Side →
1Best MatchPathfinding· Intermediate

A* Search

Full profile →

Finds a low-cost path by combining path cost so far with a heuristic estimate to the goal.

Avg Time
O(E)
Worst Time
O(E)
Space
O(V)

Why it fits your problem:

  • Use it when a good admissible or consistent heuristic is available.
  • Uses admissible or informed heuristics to focus expansions toward the destination.
Tradeoff / Watch out for: Avoid it when the heuristic is misleading enough to behave like brute-force search.
View empirical TypeScript benchmarks for Pathfinding →
2Best MatchPathfinding· Advanced

Jump Point Search

Full profile →

Speeds up grid pathfinding by skipping over symmetric intermediate nodes.

Avg Time
O(E)
Worst Time
O(E)
Space
O(V)

Why it fits your problem:

  • Use Jump Point Search when you need to skip straight-line grid nodes.
  • Uses admissible or informed heuristics to focus expansions toward the destination.
Tradeoff / Watch out for: Avoid it when edge costs or movement constraints do not match the algorithm's assumptions.
View empirical TypeScript benchmarks for Pathfinding →
3Best MatchPathfinding· Advanced

Theta*

Full profile →

Finds smoother grid paths by allowing line-of-sight shortcuts during A* search.

Avg Time
O(E log V)
Worst Time
O(E log V)
Space
O(V)

Why it fits your problem:

  • Use when grid paths should be smoother than standard A* without full continuous planning.
  • Uses admissible or informed heuristics to focus expansions toward the destination.
Tradeoff / Watch out for: Avoid when movement must stay strictly on grid edges.
View empirical TypeScript benchmarks for Pathfinding →
4Best MatchPathfinding· Beginner

Greedy Best-First Search

Full profile →

Expands the node that appears closest to the goal by heuristic estimate.

Avg Time
O(E)
Worst Time
O(E)
Space
O(V)

Why it fits your problem:

  • Use Greedy Best-First Search when you need to rank frontier nodes by estimated remaining cost.
  • Uses admissible or informed heuristics to focus expansions toward the destination.
Tradeoff / Watch out for: Avoid it when edge costs or movement constraints do not match the algorithm's assumptions.
View empirical TypeScript benchmarks for Pathfinding →
5Best MatchPathfinding· Intermediate

Bidirectional Dijkstra

Full profile →

Finds a shortest path by running Dijkstra search forward from the start and backward from the goal.

Avg Time
O(E log V)
Worst Time
O(E log V)
Space
O(V)

Why it fits your problem:

  • Use when route planning needs two-frontier search.
  • Uses admissible or informed heuristics to focus expansions toward the destination.
Tradeoff / Watch out for: Avoid when edge costs or movement rules change faster than the search can update them.
View empirical TypeScript benchmarks for Pathfinding →
6Best MatchPathfinding· Intermediate

Weighted A*

Full profile →

Weighted A* finds routes through graphs, grids, or motion-planning spaces.

Avg Time
O(E log V)
Worst Time
O(E log V)
Space
O(V)

Why it fits your problem:

  • Use Weighted A* when the map, costs, and movement rules match its search assumptions.
  • Uses admissible or informed heuristics to focus expansions toward the destination.
Tradeoff / Watch out for: Avoid when the heuristic or cost model does not match the real movement constraints.
View empirical TypeScript benchmarks for Pathfinding →