Following are some of the viewgraphs that
are used in classroom. The links provided
below are PDF files.
- Introduction to design and
optimization.
Lecture-01:
- Mathematical formulation of
design optimization problems.
Lecture-02:
- Graphical solution of
optimization problems.
Lecture-03:
- Multicriteria optimization.
Lecture-04:
- Fundamental concepts of
optimality-I. Gradient
vector, Hessian matrix, Taylor series expansion, Quadratic forms, and
Eigenvalues of matrices.
Lecture-05:
- Fundamental concepts of
optimality-II. Necessary and
sufficient conditions for optimality of unconstrained and equality
constrained problems.
Lecture-06:
- Fundamental concepts of
optimality-III. Necessary and
sufficient conditions for optimality of constrained problems, Kuhn-Tucker
conditions, and post optimality analysis.
Lecture-07:
- Fundamental concepts of
optimality-IV. Global
optimality, convex functions, convex programming problems.
Lecture-08:
- Linear Programming (LP) and
Sequential Linear Programming (SLP) with move limits implementation.
- One dimensional minimization;
Bracketing, polynomial interpolation and Golden section search.
Lecture-09:
- REVIEW - I:
- Unconstrained Minimization:
Nelder and Mead's Sequential Simplex, Powell's Conjugate Directions,
Steepest Descent, Fletcher-Reeves Conjugate Gradient, Newton's, and BFGS
Quasi-Newton Algorithms.
- Sequential Unconstrained
Minimization Techniques (SUMT) for n-dimensional constrained function
minimization; Exterior, Interior, and Extended Interior Penalty function
approaches.
Lecture-11:
- Method of Multipliers
(Augmented Lagrange Multiplier Method) for equality and inequality
constrained problems.
Lecture-12:
- REVIEW - II:
- Method of Feasible
Directions.
Lecture-13:
- The Gradient Projection
Method.
- REVIEW - Final Exam:
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