L-1.2: What is Algorithm | How to Analyze an
Algorithm | Priori vs Posteriori Analysis | DAA
Algorithm: Definition and Characteristics
An algorithm is a finite set of steps used to solve a particular
problem. It can be defined as a blueprint for solving a problem,
regardless of the programming language used. An algorithm can
be seen as having a finite number of steps to solve a particular
problem, such as finding the sum of two numbers.
Algorithm should contain a finite number of instructions and
each instruction should take finite time to execute.
It should contain relevant and unambiguous instructions.
Algorithmic analysis is an important part that involves
determining the time and space complexities of an algorithm.
Execution time can be calculated through prior or posterior
analysis.
Asymptotic notations such as big O, big omega, and small
omega are used to represent the time complexity of an
algorithm.
Comparing two algorithms based on the time and space they use
is an important part of algorithmic analysis. The analysis should
be independent of a particular hardware, as it can affect the
execution time. Uniform values should be used to represent the
execution time for the analysis. Asymptotic notations are used to
represent the time complexity of an algorithm.
Algorithm | Priori vs Posteriori Analysis | DAA
Algorithm: Definition and Characteristics
An algorithm is a finite set of steps used to solve a particular
problem. It can be defined as a blueprint for solving a problem,
regardless of the programming language used. An algorithm can
be seen as having a finite number of steps to solve a particular
problem, such as finding the sum of two numbers.
Algorithm should contain a finite number of instructions and
each instruction should take finite time to execute.
It should contain relevant and unambiguous instructions.
Algorithmic analysis is an important part that involves
determining the time and space complexities of an algorithm.
Execution time can be calculated through prior or posterior
analysis.
Asymptotic notations such as big O, big omega, and small
omega are used to represent the time complexity of an
algorithm.
Comparing two algorithms based on the time and space they use
is an important part of algorithmic analysis. The analysis should
be independent of a particular hardware, as it can affect the
execution time. Uniform values should be used to represent the
execution time for the analysis. Asymptotic notations are used to
represent the time complexity of an algorithm.
