|
A. Finding a zero of f' |
||
|
B. Approximating f by a polynomial P and differentiating P |
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|
C. Differentiating a Fourier transform of f and then applying an inverse transform |
||
|
D. Finding the slope of f at two randomly generated points |
||
|
E. None of the above |
|
A. 2.0 |
||
|
B. 2.1 |
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|
C. 1.9 |
||
|
D. 0.21 |
||
|
E. 0.19 |
|
A. Every difference formula has a truncation error formula that can be minimized. |
||
|
B. High degree interpolating polynomials oscillate too much. |
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|
C. Underflows increase as |
||
|
D. Overflows increase as |
||
|
E. Rounding errors increase as |
|
A. 2.000 |
||
|
B. 1.999 |
||
|
C. 1.989 |
||
|
D. 1.899 |
|
A. Centered difference formula at each |
||
|
B. 3-point difference formulas, forward difference at left endpoint, backward difference at right endpoint, and centered difference elsewhere |
||
|
C. Backward difference on left half of gridpoints, and forward difference on right half |
||
|
D. Alternating forward and backward differences, beginning with backward difference at |
|
A. |
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|
B. |
||
|
C. |
||
|
D. |
|
A. 2.0 |
||
|
B. 2.1 |
||
|
C. 1.9 |
||
|
D. 0.21 |
||
|
E. 0.19 |
|
A. They use a Taylor polynomial for f'. |
||
|
B. They integrate a piecewise polynomial approximation to f. |
||
|
C. They are a type of Monte Carlo method. |
||
|
D. They require orthogonal polynomial integration. |
||
|
E. None of the above |
|
A. 0.3750 |
||
|
B. 0.5000 |
||
|
C. 0.6667 |
||
|
D. 0.3333 |
||
|
E. 0.2500 |
|
A. 0.3750 |
||
|
B. 0.5000 |
||
|
C. 0.3333 |
||
|
D. 1.500 |
||
|
E. 0.3125 |
|
A. Subdivides if the integral is too large. |
||
|
B. It subdivides if the derivative is too large. |
||
|
C. It subdivides if rounding error exceedes truncation error. |
||
|
D. It subdivides if error estimate is too large. |
|
A. Using a left sided Riemann sum |
||
|
B. Approximating f by a polynomial P and integrating P |
||
|
C. Integrating a Fourier transform of f and then applying an inverse transform |
||
|
D. Finding the average height of f at n randomly generated points |
||
|
E. None of the above |
|
A. 13.02 |
||
|
B. 13.01 |
||
|
C. 13.0 |
||
|
D. 13.1 |
||
|
E. None of the above |
|
A. 31% |
||
|
B. 0.449 |
||
|
C. 44.9% |
||
|
D. 0 |
||
|
E. 1.449 |
|
A. 1 |
||
|
B. 0.4 |
||
|
C. 0 |
||
|
D. 0 |
||
|
E. 1.3 |
|
A. t-g |
||
|
B. g |
||
|
C. All t |
||
|
D. 0 |
||
|
E. It depends on the compiler. |
|
A. Both commutative and associative |
||
|
B. Commutative but not associative |
||
|
C. Neither commutative nor associative |
||
|
D. Associative but not commutative |
|
A. Swamping does not violate the fundamental axiom of floating point arithmetic, but cancellation does. |
||
|
B. Cancellation loses precision, while swamping does not. |
||
|
C. Swamping loses precision, while cancellation does not. |
||
|
D. Swamping can only happen with multiplication and cancellation only with addition. |
|
A. Underflow |
||
|
B. Overflow |
||
|
C. The distance between 1 and the nearest float to 1 |
||
|
D. 1/Overflow |
|
A. y underflowed. |
||
|
B. y=0. |
||
|
C. |y| is less than |x|*(machine epsilon). |
||
|
D. fl(0+y)=0. |
|
A. The floats are farther apart but have a larger range. |
||
|
B. The floats are nearer each other but have a smaller range. |
||
|
C. There are more floats. |
||
|
D. Underflow is smaller. |
||
|
E. The machine precision grows. |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
||
|
E. |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
|
A. Highly accurate |
||
|
B. Rounding correct |
||
|
C. Backward stable |
||
|
D. Well conditioned |
||
|
E. Robust |
|
A. Swamping |
||
|
B. Machine epsilon |
||
|
C. Truncation error |
||
|
D. Cancellation |
||
|
E. None of the above |
|
A. Backward stable |
||
|
B. Well conditioned |
||
|
C. Robust |
||
|
D. Ill conditioned |
||
|
E. Highly accurate |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
|
A. Problem is stable and method is backward stable. |
||
|
B. Problem is stable and method is well conditioned. |
||
|
C. Problem is well conditioned and method is well conditioned. |
||
|
D. Problem is well conditioned and method is backward stable. |
|
A. Cancellation limit |
||
|
B. Machine epsilon |
||
|
C. Underflow |
||
|
D. Backward error |
||
|
E. None of the above |
|
A. 0.003 |
||
|
B. 0.00324 |
||
|
C. 0.003242 |
||
|
D. 0.00 |
||
|
E. None of the above |
|
A. 142 |
||
|
B. 142.324 |
||
|
C. 142.3 |
||
|
D. 142.32 |
||
|
E. None of the above |
|
A. Chopping |
||
|
B. Cancellation |
||
|
C. Truncation Error |
||
|
D. Swamping |
||
|
E. Tail Error |
|
A. |
||
|
B. |
||
|
C. Given |
||
|
D. Find |
||
|
E. |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
|
A. An interpolator and a numerical differentiation rule |
||
|
B. A quadrature rule and an IVP solver |
||
|
C. A root finder and a quadrature rule |
||
|
D. An IVP solver and a root finder |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
||
|
E. None of the above |
|
A. Because of rounding errors |
||
|
B. Because of truncation errors |
||
|
C. As |
||
|
D. As |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
||
|
E. None of the above |
|
A. Because it averages 3 values in |
||
|
B. Because Euler's method is unstable |
||
|
C. Because the corrector needs a prediction |
||
|
D. Because a 3-step method needs 2 previous approximations to y |
||
|
E. Because a 3-step method needs 3 previous approximations to y |
|
A. Euler's method is unstable. |
||
|
B. Eulers method has local truncation error |
||
|
C. Euler's method has large rounding errors. |
||
|
D. Euler's method is too slow for a 3 step method. |
||
|
E. Taylor methods are more general. |
|
A. The corrector is typically a higher order Runge Kutta method. |
||
|
B. The corrector is typically a low order Runge Kutta method. |
||
|
C. The corrector is typically an implicit method. |
||
|
D. The corrector is typically a higher order explicit multistep method. |
|
A. 1.297 |
||
|
B. 1.015 |
||
|
C. 0.7650 |
||
|
D. 1.547 |
|
A. 1 |
||
|
B. 2 |
||
|
C. 4 |
||
|
D. 5 |
||
|
E. 6 |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
|
A. Taylor's method |
||
|
B. Adams-Bashforth's method |
||
|
C. Adams-Moulton's method |
||
|
D. Runge-Kutta's method |
|
A. 3.00 |
||
|
B. 3.75 |
||
|
C. 2.00 |
||
|
D. 4.55 |
||
|
E. 6.30 |
|
A. A good starting guess |
||
|
B. An error estimate |
||
|
C. Multistep method |
||
|
D. A list of allowable step sizes |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
|
A. 1 |
||
|
B. 2 |
||
|
C. 4 |
||
|
D. 5 |
||
|
E. 6 |
|
A. 1 |
||
|
B. 2 |
||
|
C. 4 |
||
|
D. 5 |
||
|
E. 6 |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
|
A. Stiff IVP's require a small timestep. |
||
|
B. Stiff IVP's require predictor-corrector methods. |
||
|
C. Stiff IVP's require Taylor methods. |
||
|
D. Stiff IVP's require high order methods. |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
|
A. Taylor methods require the evaluation of |
||
|
B. Taylor polynomials oscilate too much. |
||
|
C. Taylor methods replace derivatives with function evaluations. |
||
|
D. Taylor methods are not parallelizable because of nested function evaluations. |
|
A. k+1 |
||
|
B. k/2 |
||
|
C. 2k |
||
|
D. k-1 |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. The initial |
||
|
E. |
|
A. Partial differential equation |
||
|
B. Ill posed differential equation |
||
|
C. Side-condition differential equation |
||
|
D. Boundary value problem |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
|
A. It has degree |
||
|
B. It is |
||
|
C. It is |
||
|
D. It is a root of |
||
|
E. It is deflated. |
|
A. Newton's, secant, bisection |
||
|
B. Newton's, bisection, secant |
||
|
C. Secant, Newton's, bisection |
||
|
D. Secant, bisection, Newton's |
||
|
E. Bisection, Newton's, secant |
|
A. The Chinese remainder method |
||
|
B. Degree slashing |
||
|
C. Synthetic division |
||
|
D. Deflation |
|
A. Newton's method is more accurate. |
||
|
B. Extracting |
||
|
C. It is pretend. |
||
|
D. Rounding errors and truncation errors work against each other. |
|
A. Its degree is 4. |
||
|
B. Its degree is no more than 4. |
||
|
C. Its degree is no more than 7. |
||
|
D. Its degree must be more than 7. |
||
|
E. None of the above |
|
A. Hermite |
||
|
B. Taylor |
||
|
C. Lagrange |
||
|
D. Piecewise linear |
||
|
E. Quadratic spline |
|
A. 2.6 |
||
|
B. 2.8 |
||
|
C. 3.9 |
||
|
D. 1.5 |
||
|
E. 2.9 |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
||
|
E. There is no error; it is well posed. |
|
A. Only one |
||
|
B. Depends upon the knot positions |
||
|
C. n+1 Lagrange basis functions |
||
|
D. Infinitely many |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
||
|
E. None of the above |
|
A. 4.1 |
||
|
B. 3.8 |
||
|
C. 3.9 |
||
|
D. 3.5 |
||
|
E. 2.9 |
|
A. Osculating polynomial |
||
|
B. Lagrange interpolator |
||
|
C. Hermite interpolator |
||
|
D. Vandermonde interpolator |
||
|
E. None of the above |
|
A. Three basis functions, each of degree 2 |
||
|
B. Two basis functions, each of degree two |
||
|
C. Two basis functions, each of degree three |
||
|
D. Three basis functions, of degree 1, degree 2 and degree 3 |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
|
A. 4 |
||
|
B. 5 |
||
|
C. 6 |
||
|
D. 7 |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
||
|
E. None of the above |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
||
|
E. |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
||
|
E. |
|
A. 10.149 |
||
|
B. 10.150 |
||
|
C. 5.5074 |
||
|
D. 9.8500 |
||
|
E. 10.001 |
|
A. n |
||
|
B. 2n |
||
|
C. n+2 |
||
|
D. n/2 |
||
|
E. None of the above |
|
A. It is close to the correct answer. |
||
|
B. It gives the bisector of the zero. |
||
|
C. It is not complex. |
||
|
D. It gives function height and slope. |
||
|
E. None of the above |
|
A. 4 |
||
|
B. 5 |
||
|
C. 6 |
||
|
D. 8 |
||
|
E. 10 |
|
A. f has exactly one zero in [a,b]. |
||
|
B. f has 0, 1, or infinitely many zeros in [a,b]. |
||
|
C. f has an even number of zeros in [a,b]. |
||
|
D. f has an odd number of zeros in [a,b]. |
|
A. Newton's method |
||
|
B. Bisection method |
||
|
C. Secant method |
||
|
D. Meuller's method |
||
|
E. False position |
|
A. Newton's method |
||
|
B. Secant method |
||
|
C. Bisection method |
||
|
D. None of the above |
|
A. Neither Newton's nor bisection can be applied here. |
||
|
B. Neither Newton's nor secant can be applied here. |
||
|
C. Bisection cannot be applied here. |
||
|
D. Newton's method cannot be applied here. |
|
A. For each iteration, Newton's method adds 1 correct bit and bisection add about 0.5 correct bits. |
||
|
B. For each of the two iterations, Newton's method doubles number of correct bits and bisection adds 2 correct bits. |
||
|
C. For each iteration, Newton's method doubles the number of correct bits and bisection adds 1 correct bit. |
||
|
D. For each iteration, Newton's method adds two correct bits and bisection adds 1 correct bit. |
|
A. Linearizing |
||
|
B. Bisecting the line from |
||
|
C. Approximating |
||
|
D. Approximating |
||
|
E. None of the above |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
||
|
E. |
|
A. It does not always converge. |
||
|
B. It may divide by zero. |
||
|
C. It requires the evaluation of f'. |
||
|
D. It may get stuck in a cycle. |
||
|
E. It requires complex arithmetic. |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
|
A. Using the secant angle between |
||
|
B. Averaging the Newton method and the bisection method |
||
|
C. Approximating |
||
|
D. Finding the x-intercept of the line joining |
||
|
E. None of the above |
|
A. k-1 |
||
|
B. k |
||
|
C. k+1 |
||
|
D. k+2 |
||
|
E. 2k |
|
A. |
||
|
B. |
||
|
C. |
||
|
D. |
||
|
E. |