In this era of Machine Learning and Data Science, students often crave for learning the basic tools of optimization theory because machine learning ideas essentially exploit the power of Numerical Linear Algebra, Optimization, and Statistics. The primary aim of this course is to hand over a complete readymade package for beginners in mathematical optimization. 'Complete' in the sense of its mathematical orientation, geometrical explanation, problem-solution sheets, and tutorial sheets. After attending this course, students will get to know the standard methods, and basic and modern results in optimization. The concepts will be explained not only with mathematical rigor but also with geometrical essence so that students feel optimization is fun. The course covers mathematical foundation for optimization and basic techniques of unconstrained and constrained optimization problems.

Lecture 1.

Introduction and history on the development of optimization theory

Section I: Tools from Linear Algebra

Lecture 2.

Vector space, basis and dimension, subspace, inner products and norms, matrix norms, projection, eigen values and eigen vectors

Lecture 3.

Definiteness of matrices: eigen value criterion and Sylvester criterion, quadratic forms and quadratic functions

Section II: Tools from Calculus of Several Variables

Lecture 4.

Sets in Rn: balls, neighborhood, interior point, open sets, closed sets, bounded sets, compact sets, level sets, epigraphs

Lecture 5.

Supremum and infimum of a set, sequence, subsequence, limsup and liminf

Week 2:

Lecture 6.

Order of convergence of a sequence

Lecture 7.

Limit, continuity, uniform continuity and Lipschitz continuity and their geometries

Lecture 8.

Partial derivative, gradient, Hessian, directional derivative

Lecture 9.

Differentiablity, hierarchy of ‘PDs, DD and diffentiability’

Lecture 10.

Little oh and big Oh notations, Taylor’s theorem

Week 3:

Section III: Convex Sets and Convex Functions

Lecture 11.

Convex sets, examples of commonly occurring convex sets, convexity preserving operations

Lecture 12.

Convexity preserving operations continued, separation result (a point and a closed convex set)

Lecture 13.

Theorems of alternatives: Farkas’ theorem

Lecture 14.

Theorems of alternatives: Gordan’s theorem and other results

Lecture 15.

Convex functions, their continuity, Lipschitz continuity and directional derivative

Lecture 16.

Zeroth order and first order characterizations of convexity

Lecture 17.

Second order characterization of convexity, convexity preserving operations

Section IV: Unconstrained Optimization Theory

Lecture 18.

Local optima and its variants, saddle point, coercive functions, existence of optima: Weierstrass theorem

Lecture 19.

Existence of optima: Theorem for coercive functions. Descent direction and its first order and second order characterizations

Lecture 20.

First order and second order necessary and sufficient conditions for minima

Week 5:

Section V: Unconstrained Optimization Methods

Lecture 21.

General structure of an optimization algorithm, global convergence, descent property, quadratic termination property. Introduction to line search: exact and inexact line searches

Lecture 22.

Inexact line searches: Armijo-Goldstein, Wolfe-Powell, Armijo-backtracking

Lecture 23.

On global convergence of gradient descent methods using line search methods: for exact line search

Lecture 24.

On global convergence of gradient descent methods using line search methods: for inexact line searches

Lecture 25.

Where do general descent methods converge? —almost always to a local minimizer. Capture theorem.

Week 6:

Lecture 26.

Scaling of variables

Lecture 27.

Practical stopping criteria

Lecture 28.

Steepest descent method, descent property, zigzagging, scaling effect, Barzilai and Borwin gradient method

Lecture 29.

Global convergence of steepest descent method and order of convergence. Scaling effect and dimension independency.

Lecture 30.

Newton method and its local convergence.

Week 7:

Lecture 31.

Dimension dependency of Newton method. Scaling effect.

Lecture 32.

Levenberg-Marquardt and other modified Newton methods

Lecture 33.

Quasi-Newton methods - quasi-Newton equation, general quasi-Newton algorithm, variable metric method, general comparison of Newton and quasi-Newton methods

Lecture 34.

Global convergence of quasi-Newton methods for uniformly convex functions

Lecture 35.

Superlinear convergence of quasi-Newton methods

Week 8:

Lecture 36.

Basic conjugate direction method

Lecture 37.

Convergence rate of the basic CG method

Lecture 38.

Trust region methods: Cauchy point, dog leg and double dog leg methods

Section VI. Constraint Optimization Theory

Lecture 39.

A revisit to Lagrange multipliers method

Lecture 40.

Cones for constraint optimization: cone, dual cone, cone of feasible directions, linearizing cone, cone of descent directions

Week 9:

Lecture 41.

Cones for constraint optimization: tangent cone. Geometric optimality conditions.

Lecture 42.

First order FJ and KKT necessary optimality conditions

Lecture 43.

First order KKT sufficient optimality condition

Lecture 44.

Second order KKT necessary and sufficient optimality conditions

Lecture 45.

Constraint qualification

Week 10:

Section VII: Lagrangian Duality Theory

Lecture 46.

Lagrangian duality: how does one discover duality? Geometric interpretation of duality.

Lecture 47.

Results on dual function: dual problem is a convex optimization problem, differentiability of the dual function

Lecture 48.

Weak duality theorem. Duality gap. Equal primal and dual objective values imply optimal.

Lecture 49.

Strong duality theorem. Convex optimization problem and SCQ implies strong duality.

Lecture 50.

Saddle point optimality and absence of duality gap. Relationship between saddle point criteria and KKT conditions

Week 11:

Section VIII: Linearly Constrained Nonlinear Optimization Algorithms

Lecture 51.

Quadratic programming methods – Active set method

Lecture 52.

Quadratic programming method – Interior point method

Lecture 53.

Projected gradient algorithm

Lecture 54.

Reduced gradient algorithm

Section IX: Nonlinearly Constrained Nonlinear Optimization Algorithms

Lecture 55.

Penalty method – Exterior penalty method

Lecture 56.

Penalty method – Interior penalty method

Week 12:

Lecture 57.

Penalty method – Augmented Lagrangian method

Lecture 58.

Sequential quadratic programming – Lagrange-Newton method

Lecture 59.

Sequential quadratic programming – Reduced Hessian matrix method

Lecture 60.

Trust region method

Lecture 61.

Null space method

1.M. S. Bazaaraa, H. D. Shirali and M. C. Shetty, Nonlinear
Programming, Theory and Algorithms, 3rd Edition, John Wiley &
Sons, New York (2004).

2.P. Drimiti Bertsekas, Nonlinear Programming, Athena Scientific, 3nd Edition, 2016

3.E. K. P. Chong and S. W. Zak, An Introduction to Optimization, 4th Edition, John Wiley & Sons, 2013.

4.J. E. Dennis Jr and R. B. Schnabel, Numerical Methods for Unconstrained Optimization and Nonlinear Equations, Society for Industrial and Applied Mathematics, 1996.

5.W. Sun and Y.-X. Yuan, Optimization Theory and Methods, Springer Science & Business Media, 2006.

2.P. Drimiti Bertsekas, Nonlinear Programming, Athena Scientific, 3nd Edition, 2016

3.E. K. P. Chong and S. W. Zak, An Introduction to Optimization, 4th Edition, John Wiley & Sons, 2013.

4.J. E. Dennis Jr and R. B. Schnabel, Numerical Methods for Unconstrained Optimization and Nonlinear Equations, Society for Industrial and Applied Mathematics, 1996.

5.W. Sun and Y.-X. Yuan, Optimization Theory and Methods, Springer Science & Business Media, 2006.

Prof. Debdas Ghosh is currently an Assistant Professor in the Department of Mathematical Sciences, Indian Institute of Technology (BHU) Varanasi, India. In 2014, he obtained Ph.D. degree from IIT Kharagpur in Mathematics. His research interest broadly includes Optimization Theory and Applications. He is a recipient of Prof. J. C. Bose Memorial Gold
Medal from IIT Kharagpur (2009). He has been awarded Outstanding Potential for Excellence in Research and Academics Award (2014) from his earlier employer, BITS-Pilani. So far, he has published forty international journal papers and fifteen conference papers. He has written a research monograph entitled An Introduction to Analytical Fuzzy Plane
Geometry which is published by Springer in the book series of Studies in Fuzziness and Soft Computing. He has edited two conference proceedings volumes on Mathematics and Computing which are published by Springer.

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.

The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).

Date and Time of Exams: **April 29, 2023** Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.

Registration url: Announcements will be made when the registration form is open for registrations.

The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.

Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.

Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Kanpur. It will be e-verifiable at nptel.ac.in/noc.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team

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