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• #REAL MAX MATLAB CODE#

n norm (X,p) devuelve la norma p de la matriz X, en la que p es 1, 2 o Inf: Si p 1, n es la suma de columna absoluta máxima de la matriz.

#REAL MAX MATLAB CODE#

Here we discuss the various examples of the if-else statements in Matlab along with different conditions and code implementation. n norm (X) devuelve la norma 2 o el valor singular máximo de la matriz X, que es aproximadamente max (svd (X)). This is a guide to IF-Else Statement in Matlab. If ( a >= min ) & & ( a = min ) & & ( a ’ ( greater than), ‘ = ’ (greater than equal l to), ‘ < = ’ ( less than equal to), ‘ & ’ ( logical and ), ‘ =! ’( not equal to), ‘ || ’ ( logical or ), etc. If ( a c ) - nested if condition 4ĭisp ( ' b is max ' ) if condition 4 is true if condition 2 is trueĭisp ( ' c is maximum ' ) -if condition 2 is false In this example, we will see a maximum of three numbers, let us consider three numbers a, b and c. Screen 2: Matlab implementation of example 2 Example #3 – Use of Nested if Statement Screen 2 shows the Matlab implementation of example 2. Let us take two number ‘ a ’ and ‘ b ’.ĭisp ( ' a is maximum ' ) - condition 1 is trueĭisp (' b is minimum ' ) -condition 1 is false f realmax returns the largest finite floating-point number in IEEE ® double precision.

y90 y (i-1)>y90 & y (i)This could otherwise give you a large speed gain (factors of 100 or more are not uncommon).Screen 1 shows the Matlab implementation of example 1. This could both be beneficial, because calls to non-builtin functions inside a loop prevent Matlab's JIT compiler from translating the loop to machine language. Also, roots does nothing more than find the eigenvalues of the companion matrix, so you could find these eigenvalues yourself, which prevents a call to roots. This allows you to find only one (or a few) roots and save time (there's a fair chance it's also possible to compute the roots or extrema of a Chebychev analytically, although I could not find a good reference for that (or even a bad one for that matter.)).Īnother attempt that you can make in speeding things up, is to note that polyder does nothing more than Pprime = (numel(P)-1:-1:1). You can modify the eig evaluation of the companion matrix in there, to eigs. It does so by interpolating the polynomial by a Chebychev polynomial, and finding its roots. There is a file exchange submission by Steve Morris which finds all real roots of functions on a given interval. I could be completely wrong here but you might just be out of luck in getting something faster unless you can provide more information of have some kind of relationship between the polynomials generated at each step. There is insufficient information available to consider calculating just a subset of roots of the derivative polynomial - how could you know which derivative root provides the maximum stationary point of the polynomial without comparing the function value at ALL of the derivative roots? If your polynomial coefficients were being perturbed at each step by only a (bounded) small amount or in a predictable manner, then it is conceivable that you would be able to try something iterative to refine the solution at each step (for example something crude such as using your previous roots as starting point of a new set of newton iterations to identify the updated derivative roots), but the question does not suggest that this is in fact the case so I am just guessing. If the coefficients of the polynomial change at every time step in an arbitrary fashion, then ultimately you are faced with a distinct and unrelated optimisation problem at every stage. I think that you are probably out of luck.