Algorithms and Data Structures Fundamentals

Study Notes

Algorithms

A set of instructions to solve a specific problem or perform a particular task
Characteristics:
- Finiteness: Algorithms must stop after a finite number of steps
- Definiteness: Each step must be precisely defined
- Effectiveness: Algorithms must be able to be performed by a computer
- Correctness: Algorithms must produce the correct output
Types of algorithms:
- Recursive algorithms: Break down problems into smaller sub-problems
- Dynamic algorithms: Solve problems by breaking them down into smaller sub-problems and using previous solutions
- Greedy algorithms: Make the best choice at each step, hoping to find a global optimum

Data Structures

Ways to organize and store data in a computer so that it can be efficiently accessed and manipulated
Types of data structures:
- Arrays: A collection of elements of the same data type stored in contiguous memory locations
- Linked lists: A collection of elements where each element points to the next element
- Stacks: A last-in, first-out (LIFO) data structure
- Queues: A first-in, first-out (FIFO) data structure
- Trees: A hierarchical data structure with nodes and edges
- Graphs: A non-hierarchical data structure with nodes and edges

Problem-solving Strategies

Approaches to breaking down complex problems into manageable parts
Strategies:
- Divide and Conquer: Break down a problem into smaller sub-problems and solve each sub-problem recursively
- Dynamic Programming: Break down a problem into smaller sub-problems and solve each sub-problem only once
- Greedy Algorithms: Make the best choice at each step, hoping to find a global optimum
- Brute Force: Try all possible solutions and choose the best one
- Backtracking: Try a solution, and if it doesn't work, backtrack and try another solution
- Memoization: Store the results of expensive function calls to avoid redundant calculations

Description

Learn the basics of algorithms, data structures, and problem-solving strategies. This quiz covers the characteristics of algorithms, types of algorithms, data structures, and approaches to breaking down complex problems.