LAISHA
Open Laisha

CUET-PG Optimization Techniques for Statisticians: practice questions

CUET-PG - Statistics · India · National Testing Agency

Original practice questions written to the published syllabus · not past papers
10worked here, free
35written to this exam in this subject
10syllabus units

What this subject covers

The optimisation theory statisticians use: linear programming with simplex and duality, transportation and assignment, integer and goal programming for farm and crop planning, non-linear optimisation with Kuhn-Tucker conditions, and the numerical optimisers inside likelihood estimation. The Agribusiness track teaches operations research for managers; this goes to the theory.

In the exams: AICE-JRF Ag Stat Unit 2 'linear programming: formulation, graphical, simplex, duality, transportation and assignment' (heavy); UPSC ISS Statistics III operations research (heavy, reported); ASRB NET Ag Stat (present)

Where the 35 questions fall

How our questions for CUET-PG spread across the units of Optimization Techniques for Statisticians. Difficulty: 5 easy, 23 medium, 7 hard.

Numerical optimisation: gradient, Newton and quasi-Newton methods6
Integer programming: branch and bound5
Dynamic programming5
Non-linear programming: Lagrange multipliers and Kuhn-Tucker conditions4
Linear programming: formulation and graphical solution3
Duality and sensitivity analysis3
Goal programming for multi-objective farm planning3
Simplex method, Big-M and two-phase methods2
Transportation and assignment problems2
Quadratic programming and risk programming (MOTAD)2

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 · Linear programming: formulation and graphical solution · medium

What shape is the feasible region of a linear programme?

  1. A non-convex polygon
  2. Any closed curve
  3. A convex polyhedron
  4. A circle or ellipse
Answer and working

C. A convex polyhedron

Intersections of half-spaces are convex polyhedra.

Further reading: Taha, Operations Research: An Introduction; Hazell and Norton, Mathematical Programming for Economic Analysis in Agriculture; Nocedal and Wright, Numerical Optimization

2 · Duality and sensitivity analysis · hard

Mustard's return stays at 30. Over what range of wheat return does the plan (4, 8) remain optimal?

  1. 30 to 60
  2. 20 to 40
  3. 0 to 40
  4. 40 to 80
Answer and working

A. 30 to 60

The plan stays optimal while c_wheat/c_mustard lies between the slopes 1 (land) and 2 (labour), i.e. 30 to 60.

Further reading: Taha, Operations Research: An Introduction; Hazell and Norton, Mathematical Programming for Economic Analysis in Agriculture; Nocedal and Wright, Numerical Optimization

3 · Transportation and assignment problems · medium

In the Hungarian method, when has an optimal assignment been reached?

  1. When one line covers all zeros
  2. After exactly n iterations
  3. When all entries are zero
  4. When n lines cover the zeros
Answer and working

D. When n lines cover the zeros

n covering lines guarantee n independent zeros for a complete assignment.

Further reading: Taha, Operations Research: An Introduction; Hazell and Norton, Mathematical Programming for Economic Analysis in Agriculture; Nocedal and Wright, Numerical Optimization

4 · Integer programming: branch and bound · medium

When is a branch-and-bound node fathomed?

  1. Infeasible, integral, or no better bound
  2. When its LP value exceeds the incumbent
  3. Only when its LP is infeasible
  4. After a fixed number of branches
Answer and working

A. Infeasible, integral, or no better bound

Each of the three conditions shows that no better integer solution can lie in that branch.

Further reading: Taha, Operations Research: An Introduction; Hazell and Norton, Mathematical Programming for Economic Analysis in Agriculture; Nocedal and Wright, Numerical Optimization

5 · Integer programming: branch and bound · medium

A binary-variable model requires that at most 2 of 5 irrigation projects be chosen. How is this written?

  1. Σδⱼ = 2.5
  2. δ₁ + δ₂ ≤ 5
  3. δ₁δ₂ ≤ 2
  4. δ₁ + δ₂ + δ₃ + δ₄ + δ₅ ≤ 2
Answer and working

D. δ₁ + δ₂ + δ₃ + δ₄ + δ₅ ≤ 2

With δⱼ ∈ {0, 1}, the sum counts chosen projects.

Further reading: Taha, Operations Research: An Introduction; Hazell and Norton, Mathematical Programming for Economic Analysis in Agriculture; Nocedal and Wright, Numerical Optimization

6 · Non-linear programming: Lagrange multipliers and Kuhn-Tucker conditions · medium

What does the Lagrange multiplier of a constraint measure?

  1. The number of binding constraints
  2. The constraint's marginal value
  3. The value of the objective
  4. The step size of the algorithm
Answer and working

B. The constraint's marginal value

λ = ∂f*/∂b, the shadow price of the constraint.

Further reading: Taha, Operations Research: An Introduction; Hazell and Norton, Mathematical Programming for Economic Analysis in Agriculture; Nocedal and Wright, Numerical Optimization

7 · Numerical optimisation: gradient, Newton and quasi-Newton methods · hard

After an MLE optimiser converges, why check that the Hessian is negative definite?

  1. To make the gradient zero
  2. To speed up the next run
  3. To confirm a true maximum
  4. To transform the parameters
Answer and working

C. To confirm a true maximum

A stationary point may be a saddle or minimum; the inverse of −H gives the standard errors only at a proper maximum.

Further reading: Taha, Operations Research: An Introduction; Hazell and Norton, Mathematical Programming for Economic Analysis in Agriculture; Nocedal and Wright, Numerical Optimization

8 · Linear programming: formulation and graphical solution · easy

In the farm LP with 12 ha land and 16 labour-days, which constraints bind at the optimum (4, 8)?

  1. Land only
  2. Labour only
  3. Neither
  4. Both land and labour
Answer and working

D. Both land and labour

4 + 8 = 12 ha and 2 × 4 + 8 = 16 days: both resources are fully used, so both have positive shadow prices.

Further reading: Taha, Operations Research: An Introduction; Hazell and Norton, Mathematical Programming for Economic Analysis in Agriculture; Nocedal and Wright, Numerical Optimization

9 · Duality and sensitivity analysis · easy

What is the dual of the dual of an LP?

  1. A transportation problem
  2. An integer programme
  3. The original primal
  4. The dual itself
Answer and working

C. The original primal

Duality is symmetric: dualising twice returns the primal.

Further reading: Taha, Operations Research: An Introduction; Hazell and Norton, Mathematical Programming for Economic Analysis in Agriculture; Nocedal and Wright, Numerical Optimization

10 · Transportation and assignment problems · easy

How many possible one-to-one assignments exist for 4 tractors and 4 fields?

  1. 4
  2. 256
  3. 16
  4. 24
Answer and working

D. 24

n! = 4! = 24; 16 = 4² counts cells, not assignments.

Further reading: Taha, Operations Research: An Introduction; Hazell and Norton, Mathematical Programming for Economic Analysis in Agriculture; Nocedal and Wright, Numerical Optimization

Ten here, 35 in the app. The Laisha app has every practice question we have written to CUET-PG in this subject, marked as you go, with timed papers, a jump-to-unanswered list and a certificate when you pass at 70 per cent.

Free for ten days, then one subscription of US$10 a month. We would rather say so here than have you find it later.

Practise the full set in Laisha →

Read the subject

Optimization Techniques for StatisticiansThe full study manual, free CUET-PGFormat, sections and how it has changed

More CUET-PG practice

Probability TheoryStatistical Methods for Agricultural SciencesCategorical Data Analysis & Generalized Linear ModelsStatistical Inference I: EstimationStatistical Inference II: Testing of Hypotheses & Sequential AnalysisNonparametric & Rank-Based MethodsProbability Distributions & Sampling DistributionsStatistical Quality Control & ReliabilityBioassay, Survival & Epidemiological StatisticsMultivariate AnalysisBayesian InferenceStochastic ProcessesReal Analysis for StatisticsDemography & Vital Statistics

Optimization Techniques for Statisticians for other exams

ICAR AICE-JRF/SRF · IndiaState statistical services · IndiaASRB NET · IndiaIndian Statistical Service Examination · IndiaGATE · IndiaIIT JAM · IndiaASRB Agricultural Research Service · IndiaIndian Statistical Institute admission tests · IndiaCSIR-UGC NET · India

Ask Laisha about this

Opens Laisha's chat with your question ready to send. Sign in or start free.