A syllabus for an "Optimization Engineering" course in a B.Tech program would typically cover the fundamentals of optimization methods, their applications in engineering, and may include topics like linear programming, non-linear programming, and numerical optimization. Specific topics and the level of depth would vary depending on the specific program and the engineering discipline. A .pdf syllabus document would outline the course structure, learning objectives, assessment methods, and a detailed breakdown of topics covered. 

Typical Syllabus Content:

• Introduction to Optimization:
  Definition, types of optimization problems (linear, non-linear, integer, etc.), optimality criteria, and the role of optimization in engineering. 
• Linear Programming:
  Formulation of linear programming problems, graphical methods, simplex method, duality, and sensitivity analysis. 
• Non-linear Programming:
  Optimization of unconstrained and constrained functions, methods like gradient descent, Newton's method, and Lagrange multipliers. 
• Numerical Optimization:
  Numerical methods for solving optimization problems, including search methods (e.g., Fibonacci search, golden section search) and optimization algorithms. 
• Discrete Optimization:
  Topics like integer programming, dynamic programming, and metaheuristic methods (e.g., genetic algorithms, particle swarm optimization). 
• Engineering Applications:
  Examples of how optimization techniques are used in various engineering disciplines, such as design, manufacturing, and operations. 
• Software Tools:
  Introduction to software packages used for optimization, like MATLAB or Python libraries. 
• Case Studies:
  Analysis of real-world optimization problems and their solutions.