21–30 of 210 posts
About the travelling salesperson problem, below is a quote from the latest Sapolsky's book Determined: A Science of Life without Free Will. I am not sure how relevant this is for software developers, but still fascinating: "An ant forages for food, checking eight different places. Little ant legs get tired, and ideally the ant visits each site only once, and in the shortest possible path of the 5,040 possible ones (i…
For now, the new algorithm hasn’t actually been used to solve any logistical problems, since it would take too much work updating today’s programs to make use of it. But for Rothvoss, that’s beside the point. “It’s about the theoretical understanding of a problem that has fundamental applications,” he said. I don't see how "it would take to much work updating today's programs". Most domain specific models call out to…
The new algorithm of R&R would need to replace the algorithms at the core of Gurobi, CPlex, etc. These tools are marvels of engineering, extremely complex, results of decades of incremental improvements. If would likely take significant research effort to even figure out a way to incorporate the new discoveries into these engines.
So many discrete optimization problems can be translated into linear programs. It's a really powerful set of tools to know, kind of like SAT solvers.
I only recently learned about linear programming. I started with PuLP and Python to get a grasp. It was one of those "How did I miss this??" moments as a developer.
Earlier quoted context omitted.
The new algorithm of R&R would need to replace the algorithms at the core of Gurobi, CPlex, etc. These tools are marvels of engineering, extremely complex, results of decades of incremental improvements. If would likely take significant research effort to even figure out a way to incorporate the new discoveries into these engines.
Why would it need to replace them? From the article, they claim they have found a way to reduce the upperbound faster when searching large Integer problems. I don't see how that effects the current searching process. All of these solvers you can enter in an upperbound yourself if you have knowledge of the problem and know a previous solution. So it seems if this is just a programmatic way of reducing the upper bound,…
This paper only has 2 authors. The other solvers are probably applying technique specific tricks and speedups, and you're working with approximate optimization, it's not that easy to move everything over.
Earlier quoted context omitted.
I only recently learned about linear programming. I started with PuLP and Python to get a grasp. It was one of those "How did I miss this??" moments as a developer.
Do you have any recommendations on where to start?
Earlier quoted context omitted.
The new algorithm of R&R would need to replace the algorithms at the core of Gurobi, CPlex, etc. These tools are marvels of engineering, extremely complex, results of decades of incremental improvements. If would likely take significant research effort to even figure out a way to incorporate the new discoveries into these engines.
Why would it need to replace them? From the article, they claim they have found a way to reduce the upperbound faster when searching large Integer problems. I don't see how that effects the current searching process. All of these solvers you can enter in an upperbound yourself if you have knowledge of the problem and know a previous solution. So it seems if this is just a programmatic way of reducing the upper bound,…
About the travelling salesperson problem, below is a quote from the latest Sapolsky's book Determined: A Science of Life without Free Will. I am not sure how relevant this is for software developers, but still fascinating: "An ant forages for food, checking eight different places. Little ant legs get tired, and ideally the ant visits each site only once, and in the shortest possible path of the 5,040 possible ones (i…
In the case of TSP, when you're trying to minimize a TSP with a Euclidean metric (i.e., each node has fixed coordinates, and the cost of the path is the Euclidean distance between these two points), then we can actually give you a polynomial-time algorithm to find a path within a factor ε of the optimal solution (albeit exponential in ε).
For now, the new algorithm hasn’t actually been used to solve any logistical problems, since it would take too much work updating today’s programs to make use of it. But for Rothvoss, that’s beside the point. “It’s about the theoretical understanding of a problem that has fundamental applications,” he said. I don't see how "it would take to much work updating today's programs". Most domain specific models call out to…
However, the solution is something very specific to the individual solver and they have their own data structures, algorithms and heuristic techniques to solve the problem. None of these are interchangeable or public (by design) and you cannot just insert some outside numbers in the middle of the solver process without being part of the solver code and having knowledge of the entire process.