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Turn a vague everyday request into a precise statement of what an algorithm is responsible for. Practice naming the input, the required output, and the goal without deciding the steps yet.
Decide what information the algorithm receives and what counts as one valid problem instance. Specify units, formats, allowed values, missing data, and limits so the task is not left to guesswork.
State what answer the algorithm must produce and how you would know it is correct. Handle cases like multiple valid answers, ties, no solution, exact answers, and acceptable approximations.
Apply the previous explanations in a guided problem.
Separate nonnegotiable rules from preferences, assumptions, and goals. Reason through constraints such as deadlines, capacities, forbidden moves, required resources, and what may be safely ignored.
Match a task to common algorithmic problem shapes: decision, search, construction, counting, and optimization. Compare how the same situation changes when you ask “is there one?”, “find one,” “build one,” or “find the best one.”
Check your understanding with a short quiz.
Represent real-world things as algorithm-friendly data: lists, sets, tables, maps, graphs, weights, and labels. Choose a model that preserves the facts the algorithm needs while leaving out irrelevant detail.
Use small examples to find ambiguity before any algorithm is written. Create normal, tiny, impossible, and tricky cases, then check whether the stated inputs, outputs, and rules give a clear expected result.
Review this chapter with practice based on your mistakes.
