Untitled Quiz

Study Notes

Algorithms are more than just a computer science branch; they are fundamental to many fields, including science, business, and technology.
An algorithm is a sequence of unambiguous instructions to solve a problem. It takes input, processes the input according to the specific instructions, and produces an output.
The input must be legitimate, and the processing must complete within a finite time. Each instruction must be precise, and able to be carried out in a finite amount of time deterministically.

Algorithms are simpler descriptions than programs: They don't require the specifics of a particular programming language's syntax or structure.
Algorithms are easier to understand and read compared to a program.

For any given problem, there can be several possible algorithms.
For each algorithm, there can be multiple implementations (programs) in different programming languages.

An algorithm is a systematic logical approach, a step-by-step procedure that guides a computer to solve a problem.
Pseudocode is a simpler version of a programming language code written in plain English or informal short phrases, helping to describe the algorithm logic before actual implementation.
A program is an exact detailed code that strictly follows the programming language's rules and syntax.

Input: Obtaining information (e.g., data from a user or an external source) are essential parts of any algorithm.
Storage: Storing information temporarily or permanently, and recalling it when needed.
Process: The central part of an algorithm, involving arithmetic calculations, repetitive processes, conditional checks (if statements) for decision-making, etc.
Output: Providing the result of the algorithm's processing to the user or external system.

Finiteness: The algorithm must terminate after a finite number of steps.
Definiteness: Steps and actions are precise, clearly defined, and free of ambiguity for each scenario.
Input: Algorithms may have zero or more inputs from a defined set of objects.
Output: Algorithms have one or more outputs, which are related to the inputs in a specific way.
Effectiveness: Algorithm steps are fundamental to be executed effectively, precisely, and in finite time.

Natural Language: Describes the algorithm with words, but can be verbose and ambiguous.
Flowcharts: Visual representation using symbols and shapes, less ambiguity but may be difficult for complex solutions.
Pseudocode: Mixture of plain English and programming constructs, commonly used to design algorithms before coding them in a specific language.
Programming Language: Implementations of algorithms using specific programming languages, but may need to express many low-level details unneeded in algorithm design.

Acquiring Data: Reading input information from various external sources.
Computation: Arithmetic and logical operations, comparisons, and calculations to process the data.
Selection: Choosing a path of actions from multiple options based on certain criteria.
Iteration: Repeatedly performing a set of actions until a specific condition is met.
Report Results: Providing outcomes to the users or systems.

Understand the Problem: Clearly define the objects and operations involved.
Decide on Computational Means: Determine suitable approaches and representations for the objects and operations.
Design an Algorithm: Develop the step-by-step procedure to solve the problem using various design techniques.
Prove Correctness: Confirm the algorithm produces correct results for all valid input within a finite time.
Analyze the Algorithm: Evaluate its efficiency regarding time and space required for processing the input.
Code the Algorithm: Implement the algorithm in a chosen programming language.

Testing Correctness: Verifying the algorithm works correctly by testing with different input sets to confirm the expected outcomes.
Proving Correctness: Formal mathematical demonstration of an algorithm's correctness for all possible inputs.
Important Problem Types: Categories of common problems, like sorting, searching, string processing, graph problems, combinatorial problems, geometric problems, and numerical problems.

Provided examples illustrate simple pseudocode algorithms for calculating averages, converting numerical grades to pass/no pass grades, determining letter grades based on numerical scores, and finding the largest number in a list.