Log T = Log K + n Log dv dy dv Where Log T is y, Log K is "b",n is "m" and Log dy is "x"
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Fit a power law model to the rheological behavior presented in the data:
| T [=]D/cm^2 | y[=]1/2 |
| 1 | 3 |
| 2 | - |
| 3 | 30 |
| 4 | 52 |
| 5 | 80 |
| 6 | 100 |
| 7 | 130 |
| 8 | 160 |
| 9 | 175 |
| 10 | 190 |
| 11 | 218 |
| 12 | 240 |
| 13 | 265 |
(The second data in the table is not necessary to solve it.)
Help yourself with matlab to solve it with the following formula: (Photo)
All with the help of matlab, I need help plz
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- Master theorem: if n< d T(n)= 4 aT(n/b)+ f (n) if n2d 1. if f(n) is O(nlos, a-e ), then T(n) is O(noE") log, a 2. if f(n) is O(n" plog, “ log* n), then T(n) is O(n8.“ log** n) k+1 3. if f(n) is Q(no, a*€ ), then T(n) is O(f(n)), provided af (n/ b) < (n) for some 8 < 1. Which of the three cases above applies to an algorithm, the running time of which is described using the following recurrence relation: T(n) = 97(n/2) + nº Type 1 for case 1, and so on. For case 2, also specify a k value after a space, e.g., "2 2" Type N/A if the Master theorem is not applicable to the recurrence relationSuppose n is a positive integer.Let A = {a, b, c} and B = {u, v}. Write a. A × B b. B × A
- 4x1+4x2+6x3+9x4=03x1+3x5=26x2+5x4+7x5=-46x1+8x4+2x5=54x5+7x2+6x3+8x1=-10Represent the set of equations in a matrix equation, Ax=b, where A is a square matrix and x and b are column vectors. Write the line of code to define matrix A for this problem matlab/octave languageO(nlgn) means that there is function f(n) that is O(nlgn) which is an upper bound for the running time at large n Select one: True FalseFor each of the following program fragments: Give an analysis of the running time (Big-Oh). Justify your answer? A. public class GFG { // Linearly search x in att[]. if x is present then //return the index, otherwise return -1 static int search (int arr[], int n, intx) { int i; for (i = 0: ii = 1 while (i < n) do s = s + i i = i * 2 enddo Is the step count dependent on which term?Consider the following function fun: int fun(int n) { int s = 0; while(n > 1) { n= n/2; s++; } return s; } O A) O(n log n) O B) O(n) C) O(n²) O D) O(log n)The following function f uses recursion: def f(n): if n <= 1 return n else return f(n-1) + f(n-2) 5 Let n be a valid input, i.e., a natural number. Which of the following functions returns the same result but without recursion? a) def f(n): a <- 0 b <- 1 if n = 0 return a elsif n = 1 return b else for i in 1..n c <- a + b a <- b b <- c return b f(n): a <- 0 i <- n while i > 0 a <- a + i + (i-1) return a f(n): arr[0] <- 0 arr[1] <- 1 if n <= 1 return arr[n] else for i in 2..n arr[i] <- arr[i-1] + arr[i-2] return arr[n] f(n): arr[0..n] <- [0, ..., n] if n <= 1 return arr[n] else a <- 0 for i in 0..n a <- a + arr[i] return aWrite a program that reads N from the user and compute the following series: (N-1)-((N-2)^2) /8+ ((N-3)^3) /27-....................+1/N^3del(int n){ j=(n/3)-1 while (n>0) for(i=n to i=j*3){ print "i"; 5. n--; }//end for 7 j=j/3; }//endwhile }//del() what is the asymptotic run time of del? anyalse each line and what is teh final run time is teh answer O(n^2/3)?The following function f uses recursion: def f(n): if n <= 1 return n else return f(n-1) + f(n-2) Let n be a valid input, i.e., a natural number. Which of the following functions returns the same result but without recursion? a) def f(n): a <- 0 b <- 1 if n = 0 return a elsif n = 1 return b else for i in 1..n c <- a + b a <- b b <- c return b b) def f(n): a <- 0 i <- n while i > 0 a <- a + i + (i-1) return a c) def f(n): arr[0] <- 0 arr[1] <- 1 if n <= 1 return arr[n] else for i in 2..n arr[i] <- arr[i-1] + arr[i-2] return arr[n] d) def f(n): arr[0..n] <- [0, ..., n] if n <= 1 return arr[n] else a <- 0 for i in 0..n a <- a + arr[i] return aSEE MORE QUESTIONS
