BFS vs DFS: BFS finds shortest path in unweighted graphs, DFS uses less memory

Graph (abstract data type)

Time complexity of BFS and DFS: O(V + E)

LSM trees optimize: write-heavy workloads by buffering writes in memory

LSM trees optimize write-heavy workloads by buffering writes in memory

non-convex loss landscapes are hard: many local minima and saddle points

Non-convex loss landscapes are hard due to many local minima and saddle points

the A* algorithm does: BFS with heuristic f(n) = g(n) + h(n)

Explores paths by combining cost to reach a node (g(n)) and estimated cost to goal (h(n))

consistent hashing does: minimizes remapping when nodes join/leave

Consistent hashing distributes data across nodes, minimizing remapping when nodes join/leave

GraphSAGE does: samples and aggregates a fixed-size neighborhood

GraphSAGE samples and aggregates a fixed-size neighborhood