Advanced Design and Analysis of Algorithm Techniques
Feasible Solution: For solving a particular problem there exists n inputs and we need to obtain a subset that satisfies some constraints. Then any subset that satisfies these constraints is called feasible solution.
Optimal solution: From a set of feasible solutions, particular solution that satisfies or nearly satisfies the objectives of the function such a solution is called optimal solution.
Objective function: A feasible solution that either minimizes or maximizes a given function is called objective function.
Greedy method is popular for obtaining the optimized solutions. The Greedy-technique is a general algorithm design strategy, built on below mentioned elements:
Configurations: Different choices, values to find
Objective function: Some configurations to be either maximized or minimized
Greedy algorithms don’t always yield an optimal solution. But sometimes they do. We will see a problem for which they do. Then we’ll look at some general characteristics of when greedy algorithms give optimal solutions.
- All problems using the greedy method have n inputs and require us to obtain a subset that satisfies some constraints
- Any subset that satisfies these constraints is a feasible solution
- A feasible solution that either minimizes or maximizes given objective function is an optimal solution
Control Abstraction for divide and conquer method is given below:
Applications in greedy method are:
Topic : Greedy method | Algorithm Techniques
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