For unconstrained optimization involving two variables the analytic approach is

# For unconstrained optimization involving two

This preview shows page 301 - 303 out of 553 pages.

For unconstrained optimization involving two variables, the analytic approach is to write what are called the “partial” derivatives of the function. We then set both partial derivatives equal to 0, which creates two equations in two unknowns. We then solve these equations to find the stationary point(s). This is only easy to do when the original function is a quadratic, because then the two equations will be linear. Otherwise, the equations will be non-linear, and all we would have ac- complished is the conversion of a nonlinear optimization problem into a problem of solving nonlinear equations. In other words, we might not be any further ahead. If we are successful in obtaining an analytical solution for the stationary point, we then need to do a second-order test, which is more complicated than it is for the single-variable case. The details on how to find partial derivatives, and how to do the second-order test, are described Appendix D . With n variables, we need to find n partial derivatives, set them equal to 0, and then solve these n equations in n unknowns. Again, some or even all of these n equations could be non-linear, and finding a closed-form solution might be impossible. Furthermore, there is a very extensive procedure for determining if a local minimum or a local maximum has been found. A brief outline is provided in Appendix D . Naturally, adding constraints complicates things further. It is important that the feasible region be convex . A region is convex if we can take any two points
290 September 1, 2015 David M. Tulett in the region, draw a line between them, and all points on the line between the two points are also in the region. For example, a sphere is a convex region. A doughnut, however, is not convex. Convexity, along with other conditions on the constraint set and the function we are optimizing, helps ensure that a local optimum is also a global optimum. The entire set of conditions for optimality is known as the Karush-Kuhn-Tucker (KKT) conditions. 11 All of this is very complex. We soon learn that closed-form analytic solutions are not usually available, and therefore we must use an algorithm. The GRG (Gen- eralized Reduced Gradient) Algorithm is a general-purpose algorithm for solving constrained multivariate optimization. We saw this earlier when solving a prob- lem with one variable and no constraints; it is built-in to the Excel Solver. When used, it solves to find a local point of optimality, and verifies that the conditions for local optimality are satisfied at that point. However, the Solver has no way of telling if the feasible region is convex, nor can it tell if the function being opti- mized only has one stationary point. Unless the user has knowledge about these things, the solution found by the Solver cannot be guaranteed to be correct, except in the sense that it’s better than all neighbouring points.

#### You've reached the end of your free preview.

Want to read all 553 pages?

• Spring '16
• David M. Tulett