2.3.1 Algorithms

What is meant by the time complexity of an algorithm?

• The amount of time required to solve a particular problem
• How difficult a problem is to solve
• How many lines of code are required to solve a problem
• How quickly a solution can be developed

What two pieces of information allow you to analyse an algorithm?

• Time Complexity
• Space Complexity
• Size Complexity
• Complex Complexity
• Simplicity Complex

How do you measure of the time complexity?

• Big-O notation
• Big-N notation
• N notation
• Small-O notation
• Small-N notation

What does the big-O notation show?

• The effectiveness of an algorithm
• The amount of time required to solve a particular problem
• How difficult a problem is to solve
• How many lines of code are required to solve a problem
• How quickly a solution can be developed

What is the Big-O notation good for?

• It allows you to predict the amount of time taken to solve an algorithm given the number of items stored
• The amount of time taken to complete an algorithm is independent from the number of elements inputted.
• The amount of time taken to complete an algorithm is independent to the number of inputted elements
• The amount of time taken to complete an algorithm is proportional to the number of items inputted to the power of n
• The amount of time taken to complete an algorithm is proportional to 2 to the power of the number of items inputted.

What does a linear time complexity mean?

• The running time of an algorithm is proportional to the number of elements inputted.

• The amount of time taken to complete an algorithm is independent to the number of inputted elements
• The amount of time taken to complete an algorithm is proportional to the number of items inputted to the power of n
• The amount of time taken to complete an algorithm is proportional to 2 to the power of the number of items inputted.
• The time taken to complete an algorithm will increase at a smaller rate as the number of elements inputted.

What does a constant time complexity mean?

• The amount of time taken to complete an algorithm is independent to the number of inputted elements
• The amount of time taken to complete an algorithm is independent from the number of elements inputted.
• The amount of time taken to complete an algorithm is proportional to the number of items inputted to the power of n
• The amount of time taken to complete an algorithm is proportional to 2 to the power of the number of items inputted.
• The time taken to complete an algorithm will increase at a smaller rate as the number of elements inputted.

What does a polynomial time complexity mean?

• The amount of time taken to complete an algorithm is proportional to the number of items inputted to the power of n
• The amount of time taken to complete an algorithm is independent to the number of inputted elements
• The amount of time taken to complete an algorithm is independent from the number of elements inputted.
• The amount of time taken to complete an algorithm is proportional to 2 to the power of the number of items inputted.
• The time taken to complete an algorithm will increase at a smaller rate as the number of elements inputted.

What does an exponential time complexity mean?

• The amount of time taken to complete an algorithm is proportional to 2 to the power of the number of items inputted.
• The amount of time taken to complete an algorithm is proportional to the number of items inputted to the power of n
• The amount of time taken to complete an algorithm is independent to the number of inputted elements
• The amount of time taken to complete an algorithm is independent from the number of elements inputted.
• The time taken to complete an algorithm will increase at a smaller rate as the number of elements inputted.

What does a logarithmic time complexity mean?

• The time taken to complete an algorithm will increase at a smaller rate as the number of elements inputted.
• The amount of time taken to complete an algorithm is proportional to 2 to the power of the number of items inputted.
• The amount of time taken to complete an algorithm is proportional to the number of items inputted to the power of n
• The amount of time taken to complete an algorithm is independent to the number of inputted elements
• The amount of time taken to complete an algorithm is independent from the number of elements inputted.