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Instructions for a homework assignment in which students are required to write a matlab function that minimizes a multivariate function using newton's method with trust region. The assignment includes directions based on nash and sofer's problem 10.17, and notes on how to implement the method, document the code, and obtain gradient and hessian values. The submission requirements and maximum points are also outlined.
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AMSC 607 / CMSC 764 Homework 2, Fall 2008 Due 2pm Tuesday October 7
Assignment: Write a Matlab function that minimizes a function using New- tonās method with trust region. Follow the directions in parts (ii), (iii), (iv), and (v) of Nash and Soferās Problem 10.17, p.340 (the 106th page of the pdf file). (In my hard-copy of the book, it is Problem 16, p.327.) It begins, āWrite a computer program for minimizing a multivariate function....ā
Notes:
In-line documentation should identify the major sections of the code and provide some detail on the method used. It is important in specifying the algorithm, identifying bugs, and providing information to someone who might need to modify the software in order to solve a slightly different problem. Note that the documentation should be an integral part of the code; in other words, it is not enough to include it in a separate document, because a potential user might not have access to that document.
If you want to use a different set of parameters, that is fine, as long as newtonmin remains āgeneral purposeā. Note that in Matlab, you pass a function name myfunction using the parameter @myfunction, and you call the function using Matlabās feval.
Maximum points:
Submission: by hard-copy, given to me after class or delivered to my office. (I will ask for an electronic copy of your programs if I need them.) Please staple in the following order: