What are the advantages and disadvantages of using a greedy algorithm in Operations Research?

No idea. Maybe it was a greedy algorithm who invited me to answer this question when I knew nothing about it? I can't think of any reason I was approached.

A greedy algorythm has no ethic or legal limits. So, it is extremely dangerous, and I see nothing useful in it. And it should be forbidden.

Sorry, I'm not I. IT so I have no knowledge in this area. My focus is Theology, community engagement, cultural issues, and relationships.

This isn't my area. Probably, with this notification, I too knew something like greedy algorithm exist. My Focus area is Marketing Communication, Strategy and Branding

Greedy algorithms are a common approach in operations research for solving optimization problems. These algorithms make a series of choices, each of which looks best at the moment, without considering future consequences. While they can be very efficient and simple to implement, their applicability and success in yielding optimal solutions depend on the nature of the problem.

"For example, if we want to pack a knapsack with the most valuable items, we can use a greedy algorithm that always picks the item with the highest value-to-weight ratio, as this will ensure that we will maximize the value of the knapsack." This is not necessarily true. What is described here is a heuristic. There is no guarantee that applying this heuristic will find the optimal solution.

Greedy algorithms are typically used in situations where: 1. Optimal substructure: The problem can be broken down into smaller subproblems, and the optimal solution to the larger problem can be constructed from optimal solutions to its subproblems. 2. Greedy-choice property: Making a locally optimal choice at each step leads to a globally optimal solution. 3. Efficient computation: The problem is computationally tractable, and a greedy approach can provide a solution in a reasonable amount of time. 4. The problem constraints are compatible with a greedy strategy: Greedy algorithms work best when there are no constraints or the constraints allow for making locally optimal choices without violating the overall problem constraints.

Greedy algorithm is easier to implement and it is very efficient in term of time and space complexity but it gives local optimal solution, so if anyone is looking for global optimal solution, using greedy algorithm is a absolute disadvantage.

I have no idea. All I know is that Algorithms in general whether greedy it satisfied (🤣) have never worked in my favor. And for the life of me I can’t figure out why I was chosen from this survey and what’s its purpose.

Most research studies where greedy algorithms are detected usually fall on the path of the researcher being biased on the results to fall on a favorable opinion. This might occur consciously or unconsciously. Therefore, researchers are advised to be aware of this fact and work with genuine outcomes without favoritism.

Greedy algorithms are often straightforward and efficient, making them suitable for solving certain types of optimization problems quickly. They are relatively easy to understand and implement compared to more complex optimization techniques. They require less memory space since they make decisions incrementally, rather than storing large amounts of data.

The greedy algorithm allows you to very quickly find a solution that fits into the constraints of the problem. Due to the properties of the algorithm and its mathematical complexity, the calculation speed is close to ordinary sorting and simple arithmetic operations. Despite the lack of guarantees of optimality in some high-dimensional problems, this can be a big plus.

Hello!I'll study on detail on my free time .Then I 'll speak about this matter later. One of the main advantages of using a greedy algorithm is that it can be very effiecient and fast,as it does not require much memory or computation.

Advantages of greedy algorithms in Operations Research: 1. Efficiency: Greedy algorithms are often efficient in terms of time complexity since they typically involve making simple, local decisions at each step. 2. Simplicity: Greedy algorithms are usually easy to understand and implement, making them suitable for solving certain types of optimization problems quickly. Disadvantages of greedy algorithms in Operations Research: 1. Lack of Global Optimality: Greedy algorithms may not always produce the optimal solution because they make decisions based solely on immediate benefit without considering the overall problem structure.

Explainability for stakeholders might be another plus point. No science degree required to grasp the idea! With that you can build trust, and ease people into accepting more „adventurous“ algorithms. Maybe offer 2-opt next 😉

Greedy algorithms may not always produce the optimal solution for a given problem. They make locally optimal choices at each step, which may not lead to the best overall solution. Also they heavily depends on the problem structure. For certain types of problems, greedy algorithms may perform poorly or fail to find a feasible solution.

Greedy algorithms are good for quick decisions, but not always the best for complex problems where you need the absolute best outcome.

A greedy algorithm does not guarantee an optimal solution even for very simple small-scale problems. It does not provide for the study of a set of permissible values, but only the ordering of its individual elements according to a certain criterion. Often, regardless, the result of a greedy algorithm is very bad and is useful only as some feasible initial solution, or as an element of more advanced algorithms that aim to find an optimum.

Advantages: It could provide a better heuristic/rule when it comes to certain problems that can't be solved in a reasonable amount of time. Disadvantage: There is no guarantee that it's an optimal solution.

Greedy algorithms do not look at a problem holistically or strategically. They are constantly making the best tactical choice for the problem directly in front of them, without considering the implications across the long term.

Operations Research is a subject in final year of chartered accountancy course. I didn't get to that level. I pulled out at Intermediate itself. Only 22% of candidates who appear for CA exams are passed. There used to be Board of Examiners who moderate the marks we score in written exam. In this I lost out.

Some examples of using greedy algorithms in Operations Research (OR) include: Traveling Salesman Problem: Start at a random city and add the next closest city to the route until all cities are visited. Huffman Coding: Add symbols to the codebook in a way that minimizes the average code length. Knapsack Problem: Select the item with the highest value-to-weight ratio and add it to the knapsack until it's full. Activity Selection Problem: Select the activity with the highest profit and add it to the schedule until the time frame is full.

I can't say a thing, either good or bad, about greedy algorithms. The evolutionary motivator for greed is known. I wrote the book. Literally. The immensity of evolutionary influence on modern behavior is fatal. Migratory humans rolled along like a wheel, all parts equal and necessary. "Collaborate or Die" is burned into human DNA Around 10,000 years ago, stationary cultures defaulted to self-interest. "Acquire or Die." The DNA blueprint for self-interest was and remains missing. Without it, 40 dead empires went on to collapse like cheap card tables, with the mathematical precision of a radium clock. Distribution channels the subconscious, but very real fear that we're dying. That's why money makes people batsh*t crazy.

Now because greedy is often easy to implement, it can be a decent construction heuristic. In terms of a MVP approach this can serve a dual purpose, to provide quick first solutions for stakeholders to check the validity of the results you produce, as well as any improvements to the output format. Then you have everything in place, to built up more elaborate solution algorithms if required. Everything else is already in place!