time and space complexity

What is an Algorithm?

• A set of instructions that, when executed, completes a defined task that solves a problem.

• The process of breaking apart a problem.

• The process of removing unnecessary detail.

• A sequence of instructions.

How do we represent the complexity of an algorithm?

• Big-R

• Big-O

• Big-N

• Big-A

What order of complexity does this graph represent?

• Exponential

• Polynomial

• Linear

• Logarithmic

What order of complexity does this graph represent?

• Exponential

• Polynomial

• Linear

• Constant

What order of complexity does this graph represent?

• Exponential

• Polynomial

• Linear

• Logarithmic

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

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 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.

How do you reduce the space complexity?

• Try to complete all of the operations on the same data set
• You reduce the amount of embedded for loops, and then reduce the amount of items you complete the operations on i.e. divide and conquer