Algorithms Overview and Characteristics

A well-defined, step-by-step procedure that solves a specific problem.

A set of instructions that are clear and unambiguous, leaving no room for interpretation.

A problem presented to an algorithm that needs to be solved. Example: adding two numbers.

Data provided to the algorithm, like numbers, words, or images.

The result produced by the algorithm after processing the input. Example: the sum of two numbers.

An algorithm terminates after a specific, limited number of steps.

An algorithm should be practical and able to be executed with the available resources.

Describes the steps of an algorithm in a structured way using keywords like 'START', 'STOP', and arrows to denote flow.

An approach to problem-solving that involves systematically trying all possible solutions until a satisfactory one is found.

An algorithm where a function calls itself repeatedly to solve a problem by breaking it into smaller, similar subproblems.

A technique that systematically explores all possible solutions by building them incrementally, backtracking when a dead end is reached.

Algorithms designed to locate specific elements or sets of elements within a data structure.

Algorithms that are independent of specific programming languages and follow a step-by-step process that can be implemented in various languages.

The process of writing an algorithm involves breaking down a problem into smaller, manageable steps and expressing them in a clear and concise manner.

Algorithms are often written with the goal of being implemented in programming code, but their design should be independent of specific languages to ensure flexibility and reusability.

Writing algorithms effectively involves utilizing common programming constructs such as loops, conditional statements, and function calls.

Algorithm writing involves an iterative process of refining and improving the steps based on the understanding of the problem domain and available resources.

Arranging data in a specific order, typically ascending or descending.

Dividing a problem into smaller sub-problems, solving each sub-problem, and combining the solutions.

Building a solution step-by-step, choosing the option that provides the most immediate benefit at each step.

Assigning a key to specific data, enabling fast retrieval.

A function that transforms data into a fixed-size value (hash code), used in hashing algorithms.

Consists of three steps: Divide, Solve, Combine.

A common example of a searching algorithm that efficiently finds data in a sorted list.

Common examples include QuickSort, MergeSort, BubbleSort, and HeapSort.

An algorithm that breaks down a problem into smaller overlapping sub-problems, solves them once, and stores their solutions to avoid redundant calculations.

An algorithm that uses a random number at least once during computation to make a decision.

A measure of algorithm performance, regardless of implementation details.

A generalized measure of algorithm performance, regardless of implementation specifics.

An algorithm that attempts to find the best possible solution, but doesn't guarantee it.

Analysis performed after the algorithm is implemented and executed.

A type of analysis that analyzes the algorithm before implementation.

A way to improve the efficiency of an algorithm by simplifying the problem.

Measures the memory required for an algorithm to execute, essentially how much space it takes up in the computer's memory.

Evaluates an algorithm's performance in the real world, considering factors like hardware, programming language, and compiler type.

Breaking down a complex problem into smaller, manageable pieces, making it easier to code and understand.