Common Algorithm Techniques

In the previous chapters we focused mostly on structures.

Now we look at algorithm design patterns.

These are repeatable ways to approach many problem types.

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Divide and Conquer

Break a problem into smaller subproblems, solve them, then combine results.

This approach powers many efficient algorithms, especially in sorting and search.

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Greedy Strategy

Make the best local choice at each step.

Greedy methods can be elegant and fast, but they are correct only for specific problem properties.

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Recursion

Recursion means solving a problem by calling the same function on smaller inputs.

It is often a natural fit for trees and divide-and-conquer logic.

graph TD
  A[Problem n] --> B[Problem n-1]
  B --> C[Problem n-2]
  C --> D[Base Case]

A valid base case is essential to avoid infinite recursion.

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Dynamic Programming (Conceptual)

Dynamic programming is useful when subproblems overlap.

Main idea:

• solve each subproblem once
• store results
• reuse results instead of recomputing

This often transforms very slow solutions into practical ones.

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Choosing a Technique

Technique choice depends on problem structure:

• independent subproblems: divide and conquer
• optimal local choices: greedy
• recursive structure: recursion
• overlapping subproblems: dynamic programming

This recognition skill is a major part of DSA maturity.

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Key Ideas to Remember

• Algorithm design uses reusable strategy patterns.
• Divide and conquer breaks then combines.
• Greedy prioritizes local best choices.
• Recursion solves smaller versions of the same problem.
• Dynamic programming caches overlapping subproblems.

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What Comes Next

Now we apply these ideas to classic tasks every engineer encounters:

sorting and searching algorithms.