bascross.blogg.se - Finding optimal portfolioin excel solver function

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As explained below, if your model is smooth convex, you can expect to find a globally optimal solution if it is smooth but non-convex, you will usually be able to find a locally optimal solution if it is non-smooth, you may have to settle for a “good” solution that may or may not be optimal. The kind of solution Solver can find depends on the nature of the mathematical relationships between the variables and the objective function and constraints (and the solution algorithm used).

But like many users, you may decide that it’s most important to find a good solution – one that is better than the solution, or set of choices, you are using now. In other cases, it will find a locally optimal solution, and in still others, it will stop after a certain amount of time with the best solution it has found so far. In the best case, it will find the globally optimal solution – but this is not always possible. Solver is designed to find feasible and optimal solutions. A locally optimal solution is one where there are no other feasible solutions “in the vicinity” with better objective function values – you can picture this as a point at the top of a “peak” or at the bottom of a “valley” which may be formed by the objective function and/or the constraints. A globally optimal solution is one where there are no other feasible solutions with better objective function values. In some problems, a feasible solution is already known in others, finding a feasible solution may be the hardest part of the problem.Īn optimal solution is a feasible solution where the objective function reaches its maximum (or minimum) value – for example, the most profit or the least cost. A solution (set of values for the decision variables) for which all of the constraints in the Solver model are satisfied is called a feasible solution.