May 28, 2014 · This video introduces the process of linear programming through a basic example by determine the maximum revenue with given constraints. Site: http://mathispower4u.com...more

This precalculus video tutorial provides a basic introduction into linear programming. It explains how to write the objective function and constraints of linear programming word problems.

Lecture starts at 8:50.Aug 23, 2016. Penn State University.

Linear programming (LP) is a method to achieve the optimum outcome under some requirements represented by linear relationships. More precisely, LP can solve the problem of maximizing or minimizing a linear objective function subject to some linear constraints.

Description: In this lecture, Professor Devadas introduces linear programming. Instructors: Srinivas Devadas

Lecture 36 - Bender's decomposition [video] Lecture 37 - Solving LP equals to solving linear inequalities [video] Lecture 38 - Preliminaries of the ellipsoid algorithm [video] Lecture 39 - The ellipsoid [video] Lecture 40 - Ellipsoid method - reduction in volume in each iteration [video] Lecture 41 - Preprocessing for the ellipsoid algorithm ...

Click on any Lecture link to view that video. These videos are provided by NPTEL e-learning initiative. 1. Introduction to Linear Programming Formulations. 2. Linear Programming Formulations (Contd...) 3. Linear Programming Solutions- Graphical Methods. 4. Linear Programming Solutions - Simplex Algorithm. 5. Simplex Algorithm-Minimization Problems.

These videos are provided by NPTEL e-learning initiative. 1. Lecture_01_Introduction to Linear Programming Problems. 2. Lecture_02_ Vector space, Linear independence and dependence, basis. 3. Lec_03_Moving from one basic feasible solution to another, optimality criteria. 4. Lecture_04_Basic feasible solutions, existence & derivation. 5.

Ans. Linear programming in mechanical engineering is a mathematical technique used to optimize the allocation of limited resources in order to achieve the best possible outcome. It involves formulating a mathematical model with linear constraints and objectives, and then using optimization algorithms to find the optimal solution.

This lecture focuses on several topics that are specific parts of optimization. These include linear programming (LP), the max-flow min-cut theorem, two-person zero-sum games, and duality.