Memoization or memoisation is an optimization technique used primarily to speed up computer programs by storing the results of expensive function calls and returning the cached result when the same inputs occur again.

Big Omega is the reverse Big O, if Big O is used to describe the upper bound (worst - case) of a asymptotic function, Big Omega is used to describe the lower bound (best-case).

(i) BigO
(ii) BigW
(Iii) Big \(\mu \)

Asymptotic Notations are languages that uses meaningful statements about time and space complexity

The best algorithm to solve a given problem is one that requires less space in memory and takes less time to execute its instructions to generate output.

A way of designing algorithm is called algorithmic strategy

The time efficiency of an algorithm is measured by different factors. For example, write a program for a defined algorithm, execute it by using any programming language, and measure the total time it takes to run.

(i) Time factor
(ii) Space factor

Analysis of algorithms and performance evaluation can be divided into two different phases:
(i) A Priori estimates: This is a theoretical performance analysis of an algorithm. Efficiency of an algorithm is measured by assuming the external factors.
(ii) A Posterori testing: This is called performance measurement. In this analysis, actual statistics like running time and required for the algorithm executions are collected.

An estimation of the time and space complexities of an algorithm for varying input sizes is called algorithm analysis.

(i) Analysis of an algorithm usually deals with the running and execution time of various operations involved.
(ii) The running time of an operation is calculated as how many programming instructions is executed per operation

Efficiency of an algorithm is defined by the utilization of time and space complexity

An algorithm that yields expected output for a valid input is called an algorithmic solution

(i) The way of defining an algorithm is called J algorithmic strategy.
(ii)  For example to calculate factorial for the given value n then it can be done by defining the function to calculate factorial once for the iteration-1 then it can be called recursively until the number of required iteration is reached.

Examples for data structures are arrays, structures, list, tuples, dictionary.