30-09-2012, 12:10 AM
DEMONSTRATING greedy ALGORITHM source code in java and c++
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greedy algorithm java source code
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DEMONSTRATING greedy ALGORITHM source code in java and c++
13-01-2016, 07:20 PM
14-01-2016, 02:35 PM
greedy algorithm java source code
Overview:
In general, greedy algorithms have five components:
A candidate (set, list, etc), from which a solution is created
A selection function, which chooses the best candidate to be added to the solution
A feasibility function, that is used to determine if a candidate can be used to contribute to a solution
An objective function, which assigns a value to a solution, or a partial solution, and
A solution function, which will indicate when we have discovered a complete solution
Greedy algorithms produce good solutions on some mathematical problems, but not on others.
Greedy algorithms should be applied to problems exhibiting these two properties:
Greedy choice propertyWe can make whatever choice seems best at the moment and then solve the subproblems that arise later. The choice made by a greedy algorithm may depend on choices made so far but not on future choices or all the solutions to the subproblem. It iteratively makes one greedy choice after another, reducing each given problem into a smaller one. In other words, a greedy algorithm never reconsiders its choices. This is the main difference from dynamic programming, which is exhaustive and is guaranteed to find the solution. After every stage, dynamic programming makes decisions based on all the decisions made in the previous stage, and may reconsider the previous stage's algorithmic path to solution.
Note:The traveling salesman problem doesn't have this property, and therefore the greedy algorithm solution isn't right for it.
Optimal substructureA problem exhibits optimal substructure if an optimal solution to the problem contains optimal solutions to the sub-problems.
Overview:
In general, greedy algorithms have five components:
A candidate (set, list, etc), from which a solution is created
A selection function, which chooses the best candidate to be added to the solution
A feasibility function, that is used to determine if a candidate can be used to contribute to a solution
An objective function, which assigns a value to a solution, or a partial solution, and
A solution function, which will indicate when we have discovered a complete solution
Greedy algorithms produce good solutions on some mathematical problems, but not on others.
Greedy algorithms should be applied to problems exhibiting these two properties:
Greedy choice propertyWe can make whatever choice seems best at the moment and then solve the subproblems that arise later. The choice made by a greedy algorithm may depend on choices made so far but not on future choices or all the solutions to the subproblem. It iteratively makes one greedy choice after another, reducing each given problem into a smaller one. In other words, a greedy algorithm never reconsiders its choices. This is the main difference from dynamic programming, which is exhaustive and is guaranteed to find the solution. After every stage, dynamic programming makes decisions based on all the decisions made in the previous stage, and may reconsider the previous stage's algorithmic path to solution.
Note:The traveling salesman problem doesn't have this property, and therefore the greedy algorithm solution isn't right for it.
Optimal substructureA problem exhibits optimal substructure if an optimal solution to the problem contains optimal solutions to the sub-problems.
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