Single-source shortest path
The single-source shortest-path problem is to find minimum-cost paths from one source vertex to every other reachable vertex in a weighted graph.
Optimization Algorithms: AI techniques for design, planning, and control problems
The problem of finding minimum-cost paths from one source node to every other reachable node in a weighted graph.
- How it is used
- Frames shortest-path computation as a blind-search foundation for routing and navigation problems.
- How deep it goes
- Explains initialization, priority ordering, relaxation, predecessors, and target-specific early stopping without covering implementation complexity.
- Examples
- Uses a toy graph with source S, nodes C, D, A, and B, plus navigation-routing applications.
- What you can do after
- Apply Dijkstra’s algorithm to compute shortest-path distances and predecessor links from a source node.
Classic Computer Science Problems in Swift: Essential techniques for practicing programmers
The single-source shortest path problem asks for the minimum-weight paths from one starting vertex to every other vertex in a weighted graph.
- How it is used
- Uses weighted graph routing to find minimum-cost paths from one starting vertex to every other vertex.
- How deep it goes
- Explains the greedy procedure, priority queue, distance updates, predecessor tracking, and path reconstruction without formal proof.
- Examples
- Demonstrates city distances and reconstructs a Los Angeles–Boston route through Riverside, Chicago, and Detroit.
- What you can do after
- Apply it via a weighted-graph implementation that returns minimum distances and predecessor edges for chosen routes.
Related concepts
- Dijkstra's algorithmaddressed by · Dijkstra's algorithm computes shortest paths from a source vertex in a weighted graph with nonnegative edge weights.
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