MAE 284 Final Exam fully solved & updated
(latest version verified for accuracy) (Questions +
Answers) Solved 100% Correct!!
Save
Practice questions for this set
Learn 1 /7 Study with Learn
In the following ODE, y is the independent variable.
dydx=5y+e^{-x}
Choose an answer
1 True 2 False
Don't know?
Terms in this set (52)
, The parabolic True
interpolation method is
based on fitting a
parabola to three points
on the function.
The golden section True
method helps to minimize
computational costs in
finding a
maximum/minimum.
The key benefit to the True
golden section method is
that we do not have to
calculate both x1 and x2 at
each iteration.
The parabolic True
interpolation method is
usually faster than the
golden section method.
Adaptive Quadrature True
adjusts to use a smaller
step size in areas where
the function changes
rapidly, and a larger step
size where the function
changes slowly.
Romberg's method is a True
Newton-Cotes method.
The error for the "normal" False
accuracy versions of the
forward, backward, and
centered finite difference
approximations is O(h).
(latest version verified for accuracy) (Questions +
Answers) Solved 100% Correct!!
Save
Practice questions for this set
Learn 1 /7 Study with Learn
In the following ODE, y is the independent variable.
dydx=5y+e^{-x}
Choose an answer
1 True 2 False
Don't know?
Terms in this set (52)
, The parabolic True
interpolation method is
based on fitting a
parabola to three points
on the function.
The golden section True
method helps to minimize
computational costs in
finding a
maximum/minimum.
The key benefit to the True
golden section method is
that we do not have to
calculate both x1 and x2 at
each iteration.
The parabolic True
interpolation method is
usually faster than the
golden section method.
Adaptive Quadrature True
adjusts to use a smaller
step size in areas where
the function changes
rapidly, and a larger step
size where the function
changes slowly.
Romberg's method is a True
Newton-Cotes method.
The error for the "normal" False
accuracy versions of the
forward, backward, and
centered finite difference
approximations is O(h).
