Why is hill climbing useful when solving complex problems?
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In numerical analysis, hill climbing is a mathematical optimization technique which belongs to the family of local search.
It is an iterative algorithm which begins with an arbitrary solution to a problem and then finds a better solution by incrementally changing a single element of the solution.
Hill climbing achieves optimal solutions in convex problems. It uses algorithms like the simplex algorithm for linear programming and binary search to solve convex problems. It is also used in artificial intelligence.
The relative simplicity of the algorithm makes it the popular choice amongst optimizing algorithms.
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Hill Climbing is a technique to solve certain optimization problems. In this technique, we start with the sub-optimal solution under the solution is improved repeatedly until some condition is maximized.
Hill climbing technique is mainly used for solving computationally hard problems. It looks only at the current state and the immediate future state. Hence this technique is memory efficient as it does not maintain a search tree.
Thus, hill climbing is useful for solving complex numbers due to the following reasons:
1. Constructing a sub-optimal solution obeying the constraint of the problem.
2. Improving the solution step by step.
3. Improving the solution until no more improvement is possible.
Hill climbing technique is mainly used for solving computationally hard problems. It looks only at the current state and the immediate future state. Hence this technique is memory efficient as it does not maintain a search tree.
Thus, hill climbing is useful for solving complex numbers due to the following reasons:
1. Constructing a sub-optimal solution obeying the constraint of the problem.
2. Improving the solution step by step.
3. Improving the solution until no more improvement is possible.
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