A. Finding a zero of f' | ||
B. Approximating f by a polynomial P and differentiating P | ||
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 | ||
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. | ||
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. | ||
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. , where with initial value | ||
B. | ||
C. Given , find . | ||
D. Find such that . | ||
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 , the truncation errors explode. | ||
D. As , the truncation errors explode. |
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 , which is lower than a 3 step method. | ||
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. bounded for all . | ||
C. bounded for all . | ||
D. . |
A. Taylor methods require the evaluation of for $k>1$. | ||
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. is smooth enough for all . | ||
B. is continuous in . | ||
C. is nonsingular. | ||
D. The initial is close enough to the solution. | ||
E. has a fixed point such that . |
A. Partial differential equation | ||
B. Ill posed differential equation | ||
C. Side-condition differential equation | ||
D. Boundary value problem |
A. . | ||
B. continuous in , and exists in . | ||
C. continuous in , and continuous at . | ||
D. well-defined at the critical point . |
A. has a power series representation on . | ||
B. is continuous on . | ||
C. has one continuous derivative on . | ||
D. has no poles on . |
A. | ||
B. | ||
C. | ||
D. |
A. for near . | ||
B. for near . | ||
C. for near . | ||
D. for near . |
A. | ||
B. | ||
C. | ||
D. |
A. It has degree or 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 roots is very slow on a computer. | ||
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. is a Hermite interpolator. | ||
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 at and finding its root | ||
B. Bisecting the line from to | ||
C. Approximating with a Taylor polynomial of degree 2 at and finding its root | ||
D. Approximating with a Lagrange interpolator and finding its root | ||
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 , and | ||
B. Averaging the Newton method and the bisection method | ||
C. Approximating with a Taylor polynomial of degree 2 at and finding its root | ||
D. Finding the x-intercept of the line joining and | ||
E. None of the above |
A. k-1 | ||
B. k | ||
C. k+1 | ||
D. k+2 | ||
E. 2k |
A. | ||
B. | ||
C. | ||
D. | ||
E. |