I guess it depends on what your application is here. Hexaly primarily focuses on Routing, Packing and Scheduling problems, which have strong Combinatorial components. My experience with those type of problems is that…
The main reason why companies might prefer Hexaly is their emphasis on quickly finding high Quality feasible primal solutions.
Amazon has been quite vocal about using Hexaly.
This is actually a pretty poor example because we can solve huge TSP instances to optimality in practice (see the concorde solver). There exist many more tricky Combinatorial problems such as packing or covering…
Yes, there are other interior point methods besides the ellipsoid method, and virtually all of them perform better for linear programming. Sometimes, the solvers will use these at the root node for very large models, as…
I guess it depends on what your application is here. Hexaly primarily focuses on Routing, Packing and Scheduling problems, which have strong Combinatorial components. My experience with those type of problems is that…
The main reason why companies might prefer Hexaly is their emphasis on quickly finding high Quality feasible primal solutions.
Amazon has been quite vocal about using Hexaly.
This is actually a pretty poor example because we can solve huge TSP instances to optimality in practice (see the concorde solver). There exist many more tricky Combinatorial problems such as packing or covering…
Yes, there are other interior point methods besides the ellipsoid method, and virtually all of them perform better for linear programming. Sometimes, the solvers will use these at the root node for very large models, as…