Problem Solving & Programming Quiz

Sequence refers to the order in which instructions are executed in a program. Iteration involves repeating a set of instructions using loops until a condition is met. Branching allows decision-making using conditional statements, enabling different paths to be followed based on conditions. 

Recursion is when a function calls itself to solve smaller instances of a problem. It is useful for tasks like tree traversal. However, it can use more memory due to the call stack. Iteration uses loops to repeat instructions and is generally more memory-efficient, though recursion can sometimes provide clearer solutions. 

Global variables are accessible throughout the program and allow data sharing, but they can lead to unintended side effects and make debugging difficult. Local variables are restricted to specific functions, improving modularity and reducing the risk of conflicts. 

Modularity involves breaking a program into smaller parts. Functions are sub-programs that return values, while procedures perform tasks without returning values. These concepts improve code organisation, readability, and reuse. 

Parameter passing by value involves passing a copy of the data to a function, so changes do not affect the original. Passing by reference involves passing the memory address, allowing the function to modify the original data. 

An IDE provides tools to help programmers write and debug code efficiently. Features include a code editor, compiler or interpreter, debugger, auto-completion, and syntax highlighting, all of which improve productivity and reduce errors. 

Object-oriented programming is based on classes and objects. Classes define attributes and methods, while objects are instances of classes. Key principles include inheritance, encapsulation, polymorphism, and abstraction. 

Abstraction in OOP hides complex implementation details and exposes only necessary features. This simplifies interaction with objects and improves code usability and maintainability. 

A problem is suitable for computational methods if it can be broken down into smaller tasks, expressed as a sequence of logical steps, and has clearly defined inputs and outputs. 

Problem recognition involves identifying situations where computational solutions can improve efficiency. It helps determine whether a problem can be automated or optimised using computing techniques. 

Problem decomposition breaks a complex problem into smaller components, making it easier to understand, develop, and test each part independently. 

Divide and conquer involves splitting a problem into smaller sub-problems, solving each one, and combining the results. For example, binary search repeatedly divides a sorted list to find a target value efficiently. 

Backtracking is a recursive technique that explores possible solutions and abandons those that fail. It is used in problems like maze solving or puzzles where multiple possibilities must be tested. 

Heuristics are problem-solving methods that use practical approaches to find good enough solutions quickly. They are useful when exact solutions are too slow but may not always produce the optimal result. 

Performance modelling predicts how systems behave under different conditions, helping optimise efficiency. Pipelining improves performance by overlapping stages of processing. Visualisation helps represent data and algorithms clearly, aiding understanding and analysis of complex problems.