eduKate Sengkang · LEARNING HALL · ALGORITHMS & COMPUTING
Algorithms Are Ways of Making Decisions Precise Enough to Run
Start with the problem. Name the state. Protect the invariant. Count the cost. Test the failure cases. Then decide whether the method still works when the data, machine, network or world becomes difficult.
Problem → representation → rule → invariant → cost → test → implementation → world return.
How to Use This Hub
1. Learn to Trace
Follow a small example by hand. Record every change of state. Predict the next move before revealing it.
2. Learn the Invariant
Identify what must remain true while the algorithm moves. That is usually where correctness lives.
3. Learn the Cost Model
Time, memory, communication, numerical error and implementation complexity are different costs. State which one matters.
4. Test the Boundary
Try empty input, repeated values, adversarial order, overflow, disconnection, concurrency, noise and scale.
Ten Permanent Computing Corridors
Use the corridors to choose a learning route. Use the Complete Index when you already know the method, structure or system you need.
1. Foundations, Correctness & Complexity
Learn how an algorithm is represented, justified, measured and tested before specialising.
2. Data Structures, Search & Indexing
Choose how information should be stored so the operations that matter remain possible and affordable.
3. Graphs, Networks, Flows & Routing
Model relationships, paths, dependencies, connectivity, assignment and movement through networks.
4. Strings, Parsing & Language Processing
Search text, represent repeated structure, recognise languages and turn symbol streams into meaning.
5. Dynamic Programming, Combinatorics & Exact Search
Break hard problems into states, exploit overlap, prune impossible routes and know when exponential work remains unavoidable.
6. Numerical Methods, Optimisation & Scientific Computing
Compute useful approximations while keeping conditioning, convergence, stability and error visible.
7. Geometry, Graphics, Imaging & Spatial Computation
Turn coordinates, shapes, pixels, surfaces and spatial relationships into robust computational decisions.
8. Probability, Randomisation, Streaming & Sketches
Use limited memory, sampled evidence and controlled probability without confusing an estimate with certainty.
9. Systems, Databases, Concurrency & Distributed Computing
Reason about work shared across processors, storage engines, networks and failure-prone machines.
10. AI, Machine Learning, Games & Decision Systems
Build systems that estimate, rank, learn, search or decide while keeping objectives, evidence and failure modes explicit.
The Complete Registry
The ten corridors are the learning map. The Complete Algorithms & Computing Index is the orphan-control layer for all 344 recovered articles published from 28 August to 1 September 2026.
Boundary and Ownership
This hub owns the computational method: representations, procedures, correctness, complexity, implementation and system behaviour. A computational method may also be useful in biology, physics, imaging or another field. In that case, the Science Hub owns the scientific question while this hub owns the algorithmic route.
When the question changes from “how does the method work?” to “what is happening inside the learner who is trying to master it?”, continue to the Learning Runtime Hub.
Algorithms and computing are one room in a larger Learning Hall. When you need the wider map, return to I Am Brave.
