f and cons are typically parsed into very efficient forms, but as long as f and the terms in cons give numerical values for numerical values of the variables, NMaxValue can often find a solution. NMaxValue supports a modeling language where the objective function f and constraints cons are given in terms of expressions depending on scalar or vector variables.If NMaxValue determines that the constraints cannot be satisfied, it returns - Infinity.If f is linear or concave and cons are linear or convex, the result given by NMaxValue will be the global maximum, over both real and integer values otherwise, the result may sometimes only be a local maximum.However, all the functions are general, and they will also work for vectors and tensors. It focuses on functions that are specific to Mathematica, and uses matrices for many of the examples. In different areas, this may be called the best strategy, best fit, best configuration and so on. This tutorial reviews the functions that Mathematica provides for building and working with matrices, vectors, and tensors.
NMaxValue is typically used to find the largest possible values given constraints. When people tell me that Mathematica isn’t fast enough, I usually ask to see the offending code and often find that the problem isn’t a lack in Mathematica‘s performance, but sub-optimal use of Mathematica.I thought I would share the list of things that I look for first when trying to optimize Mathematica code.NMaxValue always attempts to find a global maximum of f subject to the constraints given.
0 kommentar(er)
