Advanced Optimization Techniques & Applications Detailed Syllabus for Thermal Engineering M.Tech first year first sem is covered here. This gives the details about credits, number of hours and other details along with reference books for the course.<\/p>\n
The detailed syllabus for Advanced Optimization Techniques & Applications M.Tech 2017-2018 (R17) first year first sem is as follows.<\/p>\n
M.Tech. I Year I Sem.<\/p>\n
UNIT- I : Single Variable Non-Linear Unconstrained Optimization:<\/strong> One dimensional Optimization methods, Uni-modal function, elimination method, Fibonacci method, golden section method, interpolation methods- quadratic & cubic interpolation methods.<\/p>\n UNIT – II : Multi Variable Non-Linear Unconstrained Optimization:<\/strong> Direct search method \u2013 Univariant Method \u2013 pattern search methods \u2013 Powell\u2019s \u2013 Hook \u2013 Jeeves, Rosenbrock search methods \u2013 gradient methods, gradient of function, steepest decent method, Fletcher reeves method. Variable metric method.<\/p>\n UNIT – III : Geometric Programming:<\/strong> Polynomials \u2013 arithmetic \u2013 geometric inequality \u2013 unconstrained G.P \u2013 constrained G.P Dynamic Programming: Multistage decision process, principles of optimality, examples, conversion of final problem to an initial value problem, application of dynamic programming, production inventory. Allocation, scheduling replacement.<\/p>\n UNIT- IV : Linear Programming:<\/strong> Formulation \u2013 Sensitivity analysis. Change in the constraints, cost coefficients, coefficients of the constraints, addition and deletion of variable, constraints. Simulation: Introduction \u2013 Types \u2013 Steps \u2013 application \u2013 inventory \u2013 queuing \u2013 thermal system.<\/p>\n UNIT- V : Integer Programming:<\/strong> Introduction \u2013 formulation \u2013 Gomory cutting plane algorithm \u2013 Zero or one algorithm, branch and bound method. Stochastic Programming: Basic concepts of probability theory, random variables \u2013 distributions \u2013 mean, variance, Correlation, co variance, joint probability distribution \u2013 stochastic linear, dynamic programming.<\/p>\n REFERENCES:<\/strong><\/p>\n\n