Some of the most important applications of differential calculus are optimization problems in which the goal is to find the optimal (best) solution. For example, problems in marketing, economics, inventory analysis, machine learning, and business are all concerned with finding the best solution. These problems can be reduced to finding the maximum or minimum values of a function using our notions of the derivative.

As models become more complicated, the functions used to describe them do as well. Many functions require more than one input to describe their output. These multivariable functions also contain maximum and minimum values that we seek to find using the tools of calculus. In this module, we will extend our optimization techniques to multivariable functions.

In mathematical optimization, the method of Lagrange multipliers is a strategy for finding the local maxima and minima of a function subject to equality constraints. It is named after the mathematician Joseph-Louis Lagrange. In this module, we develop the theory and work through examples of this powerful tool which converts a constrained problem into a form such that the derivative test of an unconstrained problem can still be applied. The relationship between the gradient of the function and gradients of the constraints rather naturally leads to a usually easier reformulation of the original problem.

We now put all our theory and practice to use in a real world problem to model the costs associated to a construction project in an effort to find the best possible price point. This project is challenging and answers may vary slightly based on the assumptions you use. Be thoughtful and clear in your report about any assumptions you make along the way.