y13 (H446) SLR26 - 2.3 Measures and methods to determine eff

What does algorithm complexity primarily consider?

• The programming language used

• The number of lines in the code

• The difficulty of coding the algorithm

• The time it takes to execute and the memory it uses

What is the primary goal when designing algorithms?

• To use the least number of variables

• To make the code as short as possible

• To minimize execution time and memory usage

• To avoid using loops

How is linear complexity represented in Big O notation?

• O(log n)

• O(n)

• O(1)

• O(n^2)

What is the significance of constants in Big O notation?

• They are crucial for determining exact execution time

• They are ignored as they don't significantly affect complexity

• They indicate the maximum size of the input

• They determine the programming language efficiency

In the context of Big O notation, what does 'n' represent?

• The memory space used by the algorithm

• The execution time of the algorithm

• The size of the input data

• The number of iterations in a loop

Which Big O notation represents the worst efficiency?

• O(1)

• O(n)

• O(2^n)

• O(log n)

What does a polynomial time complexity O(n^2) indicate?

• Execution time is directly proportional to input size

• Execution time increases quadratically with input size

• Execution time is constant regardless of input size

• Execution time doubles with each additional input

What does O(1) complexity indicate about an algorithm?

• Its execution time remains constant regardless of input size

• Its execution time decreases with an increase in input size

• Its execution time doubles with each additional input

• Its execution time increases linearly with input size

Which complexity is better between O(n) and O(n^2)?

• O(n^2)

• Cannot be determined

• O(n)

• They are equally efficient

What does logarithmic complexity O(log n) best describe?

• An algorithm with constant execution time

• An algorithm that halves the input size with each step

• An algorithm where execution time doubles with each addition to the input

• An algorithm that increases execution time linearly with input size