Frontline solver optimization problem types

So if D1 is a linear function of the variables, then the IF expression is also a linear function of the variables. But if C1 doesn't depend on the variables, then only D1 or 2*D1 - not both - can be selected during the optimization. An Excel formula such as =IF(C1>10,D1,2*D1) is nonsmooth if C1 depends on the decision variables. When evaluating your objective and constraint formulas, bear in mind that only the parts of formulas that are dependent on the decision variables count. Global optimization methods are designed to solve non-convex problems. Integer and constraint programming problems are inherently non-convex. Some but not all smooth nonlinear functions are convex. Integer and Constraint Programming ProblemsДЄll linear functions and some quadratic functions are convex, and the cone constraints in conic optimization problems are also convex functions.Quadratic Constraints and Conic Optimization Problems.Linear and Quadratic Programming Problems.To learn more about each type of problem, please click the topics below. If any functions are non-convex, the problem is much harder and you cannot be certain of any of these things.Д«elow is a list of optimization problem types, arranged in order of increasing difficulty for the solution methods. If the objective and all constraints are convex, you can be confident of determining whether there is a feasible solution, finding the globally optimal solution, and solving the problem up to very large size. In an optimization problem, the types of mathematical relationships between the objective and constraints and the decision variables determine how hard it is to solve, the solution methods or algorithms that can be used for optimization, and the confidence you can have that the solution is truly optimal.ДЄ key issue is whether the problem functions are convex or non-convex: Click Convex Optimization Problems to learn more.

Frontline solver optimization problem types