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Fundamentals of Algorithms (CS502)

Graded Discussion Board (GDB)

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GDB of the subject Fundamentals of Algorithms (CS502) is going to upload in a couple of days, before submitting the GDB, please read all instructions thoroughly.

The GDB will remain open for two days (48 hours).

You may submit your GDB from “August 17, 2020 To August 18, 2020, 11:59 PM”.

GDB Topic:

Consider the scenario where you need to climb Margala Hills. You have a map pertaining multiple hiking trails like, trail 1, trail 2 etc. in order to reach at the top of hill. You have limited time and want an optimal choice to climb the hill without getting too much tired. You have the following three strategies in your toolkit to accomplish this task.

a) Dynamic programming

b) Greedy strategy

c) Divide and Conquer strategy

In the given scenario which strategy would be more appropriate and produce optimal solution, comment with proper reasons.


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Cs 502 GDB Correct Solution spring 2020

CS502 Fundamentals of Algorithms GDB Solution & Discussion Spring 2020

CS502 GDB Solution idea:


In my opinion, a greedy strategy is appropriate for given scenario and gives optimal solution. A greedy algorithm is an algorithm in which at each stage a locally optimal choice is made. A greedy algorithm is therefore one in which no overall strategy is followed, but you simply do whatever looks best at the moment. For example, a mountain climber using the greedy strategy to climb Everest would at every step climb in the steepest direction. From this analogy we get the computational search technique known as hill-climbing.

Hill climbing can often produce a better result than other algorithms when the amount of time available to perform a search is limited, such as with real-time systems, so long as a small number of increments typically converge on a good solution (the optimal solution or a close approximation).

Hill climbing is an anytime algorithm, it can return a valid solution even if it's interrupted at any time before it ends. The basic principle in hill climbing is to guess which change would improve the situation and test the change. Usually, the likely change is a small change, because fig-urinig out a good big change would be way too much work or require guesswork.


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