Computational Physics at Carleton University Index
Course Information Numerical Methods Monte Carlo Methods Statistics for Physicists Special Topics
Minimization

Minimization

The general problem discussed in this section is to find the point that minimizes a single or multi-dimensional function with as few calls as possibile. This is required when doing statistical analyses of data and in optimization problems. Many general methods to solve this problem are explored in this section, including golden section search, Brent's method, simplex method, Powell's method, and gradient methods. For multidimensional problems, the importance of minimizing along conjugate directions is demonstrated.

Linear programming (optimization) using the simplex method is explained. Simulated annealing is applied to solve the traveling salesman problem. A Java applet demonstrates the solution to this problem in real time.

Reference:

Numerical Recipies, chapter 10.

By clicking the above thumbnail page images, you can peruse the slides. You can also download the (330K) colour postscript file that contains these slides.

The entire set of slides for this course is available in colour postscript or in black and white postscript.