Algorithms Homework Help

About Algorithms

An algorithm is a finite sequence of steps that solves a problem, and these questions are about writing them, tracing them and judging how fast they are. Sorting and searching dominate, with Big-O notation, recursion and the flowchart-and-pseudocode work that goes with them.

Popular topics in Algorithms

• Searching

  Linear and binary search, the precondition binary search needs, and their comparative cost.

• Sorting

  Bubble, selection, insertion, merge and quick sort: how each works and what each costs.

• Big-O Notation

  Describing growth, best, average and worst cases, and comparing O(n), O(n log n) and O(n²).

• Recursion

  Base case and recursive case, factorial and Fibonacci, and recursion against iteration.

• Pseudocode & Flowcharts

  Expressing an algorithm precisely, standard symbols, and tracing to an output.

• Algorithm Design

  Divide and conquer, greedy methods, and dynamic programming with worked examples.

How ScanSolve helps with Algorithms

ScanSolve traces an algorithm rather than asserting its result: the array after each pass of a sort, the range remaining after each step of a binary search. That trace is both the safest way to answer and the working these questions ask to see.

Complexity answers say what is being counted — comparisons, swaps, recursive calls — before giving the order, and where two algorithms are compared, the comparison names the input size at which the difference matters.

Study tips for Algorithms

1. 01

   Show the array after every pass when tracing a sort. The passes are the marks.

2. 02

   Check that every recursion has a base case and that the recursive call moves towards it.

3. 03

   Say what you are counting before stating a complexity. "O(n²) comparisons" is an answer; "O(n²)" alone often is not.

4. 04

   Remember binary search needs sorted data. Questions include unsorted arrays specifically to test it.

Frequently asked Algorithms questions

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