{"id":7818,"date":"2019-12-15T03:28:19","date_gmt":"2019-12-15T03:28:19","guid":{"rendered":"https:\/\/www.inspirenignite.com\/vtu\/optimization-techniques-as-7th-sem-syllabus-for-vtu-be-2017-scheme-professional-elective-4\/"},"modified":"2019-12-15T03:28:19","modified_gmt":"2019-12-15T03:28:19","slug":"optimization-techniques-as-7th-sem-syllabus-for-vtu-be-2017-scheme-professional-elective-4","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/vtu\/optimization-techniques-as-7th-sem-syllabus-for-vtu-be-2017-scheme-professional-elective-4\/","title":{"rendered":"Optimization Techniques AS 7th Sem Syllabus for VTU BE 2017 Scheme (Professional Elective-4)"},"content":{"rendered":"

Optimization Techniques detail syllabus for Aerospace Engineering (As), 2017 scheme is taken from VTU<\/a> official website and presented for VTU students. The course code (17AS751), and for exam duration, Teaching Hr\/week, Practical Hr\/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.<\/p>\n

For all other as 7th sem syllabus for be 2017 scheme vtu you can visit AS 7th Sem syllabus for BE 2017 Scheme VTU Subjects<\/a>. For all other Professional Elective-4 subjects do refer to Professional Elective-4<\/a>. The detail syllabus for optimization techniques is as follows.<\/p>\n

Course Objectives:<\/h4>\n

This course will enable students to<\/p>\n

    \n
  1. Understand the unconstrained and constrained minimization.<\/li>\n
  2. Comprehend the direct search methods, discrete and dynamics programming.<\/li>\n
  3. Acquire the knowledge on finite element based optimization.<\/li>\n<\/ol>\n

    Module 1 <\/h4>\n

    For complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

    Module 2 <\/h4>\n

    Unconstrained Minimization: Multivariable functions. Necessary and sufficient conditions for optimality. Convexity. Steepest Descent Method -Convergence Characteristics. Conjugate Gradient Method. Linear programming -Simplex Method.\n<\/p>\n

    Module 3 <\/h4>\n

    Constrained Minimization: Non-linear programming. Gradient based methods. Rosens’s gradient, Zoutendijk’s method, Generalised reduced gradient, Sequential quadratic programming. Sufficient condition for optimality.\n<\/p>\n

    Module 4 <\/h4>\n

    For complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

    Module 5 <\/h4>\n

    Optimisation Application: Transportation problem. Transportation simplex method. Network problems. Maximum flow in net works. General definition of dynamic programming. Problem modeling and computer implementation.
    \nFinite Element Based Optimisation: Parameter optimization using gradient methods -Derivative calculation. Shape optimisation. Topology optimisation of continuum structures.\n<\/p>\n

    Course Outcomes:<\/h4>\n

    After studying this course, students will be able to:<\/p>\n

      \n
    1. Identify the unconstrained and constrained minimization effect of fluid properties.<\/li>\n
    2. Apply the direct search methods, discrete and dynamics programming.<\/li>\n
    3. Classify the optimisation application.<\/li>\n<\/ol>\n

      Graduate Attributes (as per NBA):<\/h4>\n