B.TechSemester 72023-24Optimization In Machine LearningKAI071
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Optimization In Machine Learning (KAI071) - AKTU Question Paper 2023-24

B.Tech · Semester 7 · Free PDF Download

This is the official AKTU Optimization In Machine Learning Previous Year Question Paper for B.Tech Semester 7, academic session 2023-24. Published by Dr. A.P.J. Abdul Kalam Technical University (AKTU/UPTU), Lucknow. Free PDF download — no login required.

Course:B.Tech
Semester:Semester 7
Session:2023-24
University:AKTU / UPTU

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Questions Asked in 2023-24

Optimization In Machine Learning (KAI071) — complete question paper · 100 marks · 3 Hours

Section AAttempt all q u e s t i o n s i n b r i e f . 2 x 10 = 20
  • a
    How does convexity play a cruci al role in optimization problems? 2
  • b
    Illustrate the application of c onvex optimization in real-world scenarios. 2
  • c
    Explain Nesterov's applicati on in convex optimization. 2
  • d
    Investigate Moreau–Yos ida regularization. 2
  • e
    Explain the regularization process. 2
  • f
    Explain the concept of dual d ecomposition in optimization 2
  • g
    How Douglas–Rachford splitti ng addresses challenges? 2
  • h
    How do optimization algorithms strat egically navigate saddle points? 2 i. What are the implications for c onvergence and optimization efficiency? 2 j. How does the structure of the optimization landscape impact th e choice of optimization algorithms?
Section BAttempt any three o f t h e f o l l o w i n g : 10 x 3 = 30
  • a
    Compare and contrast linear p rogramming, second-order cone p rogramming, and semidefinite programming. Provide real-world examples where each type of convex program is applicable
  • b
    Explain the concept of duality in convex optimization. How d oes duality provide insights into the primal and dua l aspects of an optimization pr oblem? Discuss the relationship between the primal and dual solutions
  • c
    Compare and contrast mirror de scent and traditional gradient descent methods. Highlight the advantages and disadvantages of mirror descent, a nd provide a real-world example where mirror descent might outperform other optimization algorithms
  • d
    Compare the convergence proper ties of Augmented Lagrangian m ethods and ADMM. Discuss scenarios where one method might be preferred over the other based on the nature of the optimization problem
  • e
    Compare and contrast Polyak–Juditsky averaging with other me thods used in stochastic gradient descent. H ow does it contribute to the conv ergence and stability of optimization algorithms, especially in the context of deep learning?
Section CAttempt any one p a r t o f t h e f o l l o w i n g : 10 x 1 = 10
  • a
    Describe the Karush-Kuhn-Tucker (KKT) conditions in the cont ext of convex optimization. How do these condi tions characterize optimality i n convex programs? Provide examples to illustrate the application of KKT conditions
  • b
    Provide detailed explanations and examples of different conv ex programs. Highlight the specific mathemat ical formulations and problem st ructures for each type of convex program
  • a
    Elaborate on the Frank–Wolfe method and its applications in constrained optimization. Discuss how the method addresses challenges posed by large- scale optimization problems and p rovide a scenario where it is particularly effective
  • b
    Explore the concept of Ordinary Differential Equations inter pretations in the context of optimization algorithms
  • a
    Discuss the role of dual methods in optimization and their a pplications in solving primal-dual problems. Pro vide an example illustrating t he use of dual methods and explain their advanta ges over primal methods in certain scenarios
  • b
    Provide an in-depth overview of proximal gradient methods an d their role in handling non-smooth and non-convex optimization problems
  • a
    Compare and contrast the Alternating Direction Method of Mul tipliers with other optimization algorithms, highlighting its strengths and weaknesses
  • b
    Describe the Douglas–Rachford splitting algorithm and its ap plication in solving convex optimization problems. Discuss situations where this algorithm is particularly effective and the conditions under which it converges
  • a
    Elaborate on the application of Langevin dynamics in Bayesia n inference. How does it relate to escaping saddle points and contribute to efficient sampling in high-dimensional spaces, particularly in the context of deep learning models?
  • b
    Examine the Bayesian interpr etation of Langevin dynamics. Ho w does Langevin dynamics connect to the posterior distribution in Baye sian models, and what are the implications f or exploring the posterior space in the context of probabilistic modeling?

Question text is extracted from the official AKTU question paper PDF above. Hindi translations are omitted — every question is printed in English in the original paper. Last verified: 2026-08-23.

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