Algorithmic Primers

These primers refresh algorithmic literacy by focusing on need-to-know algorithms and mental models. Each captures one computational concept that informs design, optimization, or architectural trade-offs.

Planned Primers

Search & Exploration

• A* — Informed pathfinding with heuristics
• Dijkstra — Shortest path in weighted graphs
• BFS/DFS — Breadth-first and depth-first traversal

Constraint & Allocation

• Graph coloring — Resource allocation under constraints
• Bipartite matching — Optimal pairing problems

Recursion & Aggregation

• Recursive aggregation — Tree and hierarchical data processing
• Divide & conquer — Problem decomposition strategies

Optimization

• Greedy methods — Local optimization strategies
• Dynamic programming — Overlapping subproblem solutions

Stochastic

• Monte Carlo — Randomized approximation methods
• Simulated annealing — Probabilistic optimization

Geometry / Spatial

• Line seeking — Linear pattern detection
• Convex hull — Boundary identification
• Nearest neighbor — Proximity search algorithms

Ordering & Dependencies

• Sorting algorithms — Comparison-based and distribution sorts
• Topological sort — Dependency ordering