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ICAR-IASRI MSc and PhD Differential Equations, Finite Differences & Numerical Analysis: practice questions

ICAR-IASRI MSc and PhD (Agricultural Statistics, Computer Application, Bioinformatics) · India · ICAR-Indian Agricultural Statistics Research Institute with the IARI Graduate School

Original practice questions written to the published syllabus · not past papers
10worked here, free
34written to this exam in this subject
11syllabus units

What this subject covers

Ordinary differential equations, interpolation, numerical differentiation and integration and root-finding: what AIEEA-PG Group 07 names explicitly, and what growth models, life tables and iterative estimation (Newton-Raphson for maximum likelihood) rely on.

In the exams: AIEEA-PG Group 07 Units II-III (heavy: differential equations, finite differences, interpolation, numerical integration, Newton-Raphson named); AICE-JRF Computer Application Unit 10 numerical analysis (heavy)

Where the 34 questions fall

How our questions for ICAR-IASRI MSc and PhD spread across the units of Differential Equations, Finite Differences & Numerical Analysis. Difficulty: 12 easy, 20 medium, 2 hard.

Calculus of finite differences and difference operators4
Interpolation: Newton forward and backward, Lagrange and divided differences4
Numerical differentiation4
Ordinary differential equations of first order: exact, linear and Bernoulli equations3
Numerical integration: trapezoidal and Simpson's rules3
Solution of non-linear equations: bisection and Newton-Raphson3
Solution of linear systems: Gaussian elimination and iterative methods3
Numerical solution of ODEs: Euler and Runge-Kutta methods3
Coordinate geometry of conics3
Higher-order linear equations: complementary function and particular integral2
Difference equations2

10 practice questions, worked

Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.

1 · Ordinary differential equations of first order: exact, linear and Bernoulli equations · easy

A population grows as dN/dt = 0.07N. Its doubling time is about:

  1. 14.3 time units
  2. 9.9 time units
  3. 0.07 time units
  4. 70 time units
Answer and working

B. 9.9 time units

Doubling time = ln 2/r = 0.693/0.07 ≈ 9.9. 14.3 is 1/r, the mean time constant; 70 forgets to divide by the rate in per cent.

Further reading: Boyce, DiPrima and Meade, Elementary Differential Equations (2017); Burden, Faires and Burden, Numerical Analysis (2016); Hildebrand, Introduction to Numerical Analysis (1987); Thornley and France, Mathematical Models in Agriculture (2007)

2 · Ordinary differential equations of first order: exact, linear and Bernoulli equations · easy

The monomolecular (Mitscherlich) model dN/dt = r(K − N), used for fertiliser response, has:

  1. An inflection at K/2
  2. An inflection at K/e
  3. No inflection point
  4. Exponential growth without limit
Answer and working

C. No inflection point

Growth rate is greatest at the start and declines steadily; logistic and Gompertz are sigmoid.

Further reading: Boyce, DiPrima and Meade, Elementary Differential Equations (2017); Burden, Faires and Burden, Numerical Analysis (2016); Hildebrand, Introduction to Numerical Analysis (1987); Thornley and France, Mathematical Models in Agriculture (2007)

3 · Higher-order linear equations: complementary function and particular integral · medium

A particular integral of y″ + 4y = sin x is:

  1. (sin x)/3
  2. (sin x)/5
  3. −(sin x)/3
  4. (sin x)/4
Answer and working

A. (sin x)/3

Replace D² by −1: f = −1 + 4 = 3.

Further reading: Boyce, DiPrima and Meade, Elementary Differential Equations (2017); Burden, Faires and Burden, Numerical Analysis (2016); Hildebrand, Introduction to Numerical Analysis (1987); Thornley and France, Mathematical Models in Agriculture (2007)

4 · Interpolation: Newton forward and backward, Lagrange and divided differences · medium

In Newton's forward formula, the variable p equals:

  1. (x − xₙ)/h
  2. x − x₀
  3. h/(x − x₀)
  4. (x − x₀)/h
Answer and working

D. (x − x₀)/h

(x − xₙ)/h is used in the backward formula.

Further reading: Boyce, DiPrima and Meade, Elementary Differential Equations (2017); Burden, Faires and Burden, Numerical Analysis (2016); Hildebrand, Introduction to Numerical Analysis (1987); Thornley and France, Mathematical Models in Agriculture (2007)

5 · Numerical differentiation · easy

The central difference approximation of f′(x) has truncation error of order:

  1. h
  2. h³
  3. h⁴
  4. h²
Answer and working

D. h²

[f(x + h) − f(x − h)]/(2h) − f′(x) = h²f‴/6 + …

Further reading: Boyce, DiPrima and Meade, Elementary Differential Equations (2017); Burden, Faires and Burden, Numerical Analysis (2016); Hildebrand, Introduction to Numerical Analysis (1987); Thornley and France, Mathematical Models in Agriculture (2007)

6 · Difference equations · medium

Characteristic root m = 3 repeated twice gives the solution:

  1. A3ⁿ + B3ⁿ
  2. A3ⁿ + Bn²
  3. A(3n)ⁿ
  4. (A + Bn)3ⁿ
Answer and working

D. (A + Bn)3ⁿ

A repeated root needs the extra factor n.

Further reading: Boyce, DiPrima and Meade, Elementary Differential Equations (2017); Burden, Faires and Burden, Numerical Analysis (2016); Hildebrand, Introduction to Numerical Analysis (1987); Thornley and France, Mathematical Models in Agriculture (2007)

7 · Solution of linear systems: Gaussian elimination and iterative methods · easy

Partial pivoting in Gaussian elimination chooses as pivot:

  1. The smallest non-zero entry in the whole matrix
  2. The diagonal entry always
  3. The largest entry in absolute value in the column
  4. The entry in the last row
Answer and working

C. The largest entry in absolute value in the column

Large pivots limit growth of rounding errors.

Further reading: Boyce, DiPrima and Meade, Elementary Differential Equations (2017); Burden, Faires and Burden, Numerical Analysis (2016); Hildebrand, Introduction to Numerical Analysis (1987); Thornley and France, Mathematical Models in Agriculture (2007)

8 · Solution of linear systems: Gaussian elimination and iterative methods · medium

For the same system, one Gauss–Seidel iteration from (0, 0) gives:

  1. (1.1, 0.99)
  2. (1.1, 1.1)
  3. (1.0, 1.0)
  4. (0.99, 1.1)
Answer and working

A. (1.1, 0.99)

x = 1.1, then y = (11 − 1.1)/10 = 0.99 using the new x.

Further reading: Boyce, DiPrima and Meade, Elementary Differential Equations (2017); Burden, Faires and Burden, Numerical Analysis (2016); Hildebrand, Introduction to Numerical Analysis (1987); Thornley and France, Mathematical Models in Agriculture (2007)

9 · Coordinate geometry of conics · medium

The parabola y² = 12x has focus:

  1. (12, 0)
  2. (0, 3)
  3. (6, 0)
  4. (3, 0)
Answer and working

D. (3, 0)

Compare with y² = 4ax: 4a = 12 gives a = 3, focus (a, 0).

Further reading: Boyce, DiPrima and Meade, Elementary Differential Equations (2017); Burden, Faires and Burden, Numerical Analysis (2016); Hildebrand, Introduction to Numerical Analysis (1987); Thornley and France, Mathematical Models in Agriculture (2007)

10 · Coordinate geometry of conics · medium

A fitted quadratic response surface in two fertilisers has an indefinite quadratic part. Its stationary point is:

  1. A maximum
  2. A saddle point
  3. A minimum
  4. Undefined
Answer and working

B. A saddle point

Indefinite quadratic forms give hyperbolic contours around a saddle.

Further reading: Boyce, DiPrima and Meade, Elementary Differential Equations (2017); Burden, Faires and Burden, Numerical Analysis (2016); Hildebrand, Introduction to Numerical Analysis (1987); Thornley and France, Mathematical Models in Agriculture (2007)

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Differential Equations, Finite Differences & Numerical AnalysisThe full study manual, free ICAR-IASRI MSc and PhDFormat, sections and how it has changed

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