LPP stands for "Linear Programming Problem". It is a mathematical optimization technique used to find the best possible solution for decision-making problems where the objective is to maximize or minimize a linear function, subject to a set of linear equality and inequality constraints.
In a linear programming problem, there are various variables that represent the decision variables, which need to be optimized based on certain criteria. These variables are subject to a set of linear constraints that define the limitations and requirements of the problem. The objective is to find the optimum values for the decision variables that either maximize or minimize the objective function.
The objective function is a linear mathematical expression that defines the goal of the problem, usually in terms of maximizing profits, minimizing costs, or optimizing resource allocation. The constraints are linear equations or inequalities that impose restrictions on the decision variables based on the problem's requirements and limitations.
The process of solving a linear programming problem involves formulating the problem into a mathematical model, applying mathematical techniques such as simplex method or interior point method to solve the model, and interpreting the obtained results to make informed decisions.
LPP has numerous applications in various fields, including operations research, management science, economics, engineering, and finance, where it is used to optimize resource allocation, production planning, investment decisions, transportation problems, and scheduling, among others.
