Maximize subject to 0≤x≤ 31. f(x)=x³60x² + 900x + 100, 14.3-7 (a) Use the first and second derivatives of f(x) to determine the critical points (along with the end points of the feasible region) where x is either a local maximum or a local minimum. (b) Roughly plot the graph of f(x) by hand over the feasible region. (c) Using x 15.5 as the initial trial solution, perform the first iteration of the basic simulated annealing algorithm pre- sented in Sec. 14.3 by hand. Follow the instructions given at the beginning of the Problems section to obtain the needed random numbers. Show your work, including the use of the random numbers. 659 A (d) Use your IOR Tutorial to apply this algorithm, starting with x = 15.5 as the initial trial solution. Observe the progress of the algorithm and record for each iteration how many (if any) candidates to be the next trial solution are rejected before one is accepted. Also count the number of iterations where a nonimproving move is accepted. 14 tv

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
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plz provide explained answer for part c and d asap

14.3-7. Consider the following nonconvex programming problem.
Maximize f(x) = x³60x² + 900x + 100,
subject to
0≤x≤ 31.
14.3-7
(a) Use the first and second derivatives of f(x) to determine the
critical points (along with the end points of the feasible region)
where x is either a local maximum of a local minimum.
(b) Roughly plot the graph of f(x) by hand over the feasible region.
(c) Using x= 15.5 as the initial trial solution, perform the first
iteration of the basic simulated annealing algorithm pre-
sented in Sec. 14.3 by hand. Follow the instructions given
at the beginning of the Problems section to obtain the needed
random numbers. Show your work, including the use of the
random numbers.
659
A (d) Use your IOR Tutorial to apply this algorithm, starting with
x = 15.5 as the initial trial solution. Observe the progress of
the algorithm and record for each iteration how many (if any)
candidates to be the next trial solution are rejected before
one is accepted. Also count the number iterations where
a nonimproving move is accepted.
ar
14
tv
Transcribed Image Text:14.3-7. Consider the following nonconvex programming problem. Maximize f(x) = x³60x² + 900x + 100, subject to 0≤x≤ 31. 14.3-7 (a) Use the first and second derivatives of f(x) to determine the critical points (along with the end points of the feasible region) where x is either a local maximum of a local minimum. (b) Roughly plot the graph of f(x) by hand over the feasible region. (c) Using x= 15.5 as the initial trial solution, perform the first iteration of the basic simulated annealing algorithm pre- sented in Sec. 14.3 by hand. Follow the instructions given at the beginning of the Problems section to obtain the needed random numbers. Show your work, including the use of the random numbers. 659 A (d) Use your IOR Tutorial to apply this algorithm, starting with x = 15.5 as the initial trial solution. Observe the progress of the algorithm and record for each iteration how many (if any) candidates to be the next trial solution are rejected before one is accepted. Also count the number iterations where a nonimproving move is accepted. ar 14 tv
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