# Optimizing optimization code in TI-84 program

As shown in a previous installment on running the Nelder-Mead optimization of the Rosenbrock function in TI-84, its hardware is not up to the challenge for problems of this kind. It is however an interesting case to learn from this program that took a considerable amount of time (in minutes), when most TI-84 programs complete in seconds, to grasp the technique in writing optimized code.

The problem used in the setting is the Rosenbrock function: Two techniques were used to try to optimize the code. The first one is to in-line the function (in this case the Rosenbrock function). In the original version this function is stored in a string value to be evaluated whenever necessary by using the TI-84 expr() function. The idea from common in-lining is borrowed to see if it works in TI-84. As shown in line 44 below, the Rosenbrock function replaced the original expr() function call. Another technique is to remove unnecessary comments. For most modern languages this is never considered a problem but for simple calculators it is speculated valuable processing time may be spent on processing comments that contribute nothing to the algorithm. Both programs returned the same results. To compare the gain in speed, the same set of initial parameters and tolerance were used in both the original and code-optimized program. Surprisingly, the inline method does not yield any gain while removing all those “:” that were used for indentation does improve the speed. The average of the original program is 183 seconds while the program with cleaned code is only 174 seconds, which is around 95% of the run time of the original program. # Implementing the Nelder-Mead method on the TI Nspire

Implementing the Nelder-Mead method on the Nspire is pretty straight forward. This is after nothing turns up in the search for such library on the Internet. A sample below using the Rosenbrog function as a test target: f(x,y) = (a-x)^2 + b(y-x^2)^2, using a=1 and b=100. p is the initial parameter set with {10,10}.

nm(“nmfunc”, “p”, p) invoke the Nelder-Mead program, and specify “nmfunc” as the name of the objective function (which defined earlier); “p” as the name of the parameter set; and p the actual parameter.

Results are stored in the resultsmatrix set. 1st element is the function value, 2nd and 3rd are the x,y results respectively.

Took 22 seconds on real Nspire for this test set.

This program also helped determine the parameters for a logistic regression problem on another occasion, where it was not possible using the built-in Nspire logistic regression function since it supports only x-list and y-list but the problem involved 3 variables.