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Turn a vague request like “plan the best route” into a precise question an algorithm could answer. You’ll practice naming the task, the thing being decided, and the result the algorithm must produce.
Use what you learned in the previous lesson to solve real-world problems.
Separate the data an algorithm is given from the story around it. You’ll identify input instances, fixed parameters, and details that should be ignored because they do not affect the answer.
Check what you understood with a short quiz.
State exactly what answer the algorithm must return, including its shape: a number, yes/no answer, ordered list, chosen item, schedule, path, or assignment. You’ll learn why “find the best option” is incomplete until the output is named.
Decide which inputs are valid before any algorithm runs. You’ll write assumptions such as “all distances are nonnegative” or “each job has one deadline,” and see how unstated assumptions can change the problem.
Distinguish the rules a solution must obey from the goal it is trying to achieve. You’ll classify requirements like capacity limits, allowed moves, deadlines, and “each item used once” as constraints, not algorithm steps.
Recognize whether the task asks for any valid answer or the best possible answer. You’ll compare feasibility problems, optimization problems, decision problems, counting problems, and listing problems using the same everyday scenario.
Turn words like “best,” “fastest,” “cheapest,” or “fairest” into a measurable objective. You’ll define what gets minimized or maximized and add tie-breakers when two answers score the same.
Replace messy real-world objects with just enough structure for an algorithm to work. You’ll model tasks as lists, sets, tables, grids, trees, or graphs depending on whether order, membership, coordinates, hierarchy, or connections matter.
Identify when the same task can be viewed as a known algorithmic pattern, such as search, sorting, matching, routing, scheduling, or selection. You’ll practice naming the pattern without jumping ahead to a specific algorithm.
Use tiny examples to check whether your problem statement is precise. You’ll create sample inputs with expected outputs and notice missing rules when two reasonable people would give different answers.
Look for unusual but valid inputs before choosing a method. You’ll reason about empty inputs, ties, duplicates, impossible cases, and minimum-size cases as part of defining what the problem must handle.
Keep the problem definition separate from the algorithm that will solve it. You’ll state what must be accomplished without committing to sorting, searching, brute force, or any other method too early.
Review this chapter with practice based on your mistakes.
