## How can we reduce LP problems?

Minimization Linear Programming Problems

- Write the objective function.
- Write the constraints. For standard minimization linear programming problems, constraints are of the form: ax+by≥c.
- Graph the constraints.
- Shade the feasibility region.
- Find the corner points.
- Determine the corner point that gives the minimum value.

## What is LP model?

linear programming, mathematical modeling technique in which a linear function is maximized or minimized when subjected to various constraints. This technique has been useful for guiding quantitative decisions in business planning, in industrial engineering, and—to a lesser extent—in the social and physical sciences.

**What is LPP optimization?**

LPP. Linear Programming Problems in maths is a system process of finding a maximum or minimum value of any variable in a function, it is also known by the name of optimization problem. LPP is helpful in developing and solving a decision making problem by mathematical techniques.

**What is optimal solution?**

An optimal solution is a feasible solution where the objective function reaches its maximum (or minimum) value – for example, the most profit or the least cost. A globally optimal solution is one where there are no other feasible solutions with better objective function values.

### What is optimization minimization?

An optimization problem involves minimizing a function (called the objective function) of several variables, possibly subject to restrictions on the values of the variables defined by a set of constraints.

### How many optimal solutions can an LP problem have?

An optimal solution to an LP is a feasible solution such that there does not exist any other feasible solution yielding a better (smaller or larger in the case of minimization and maximization, respectively) objective function value. An LP may have zero, one, or an infinite number of optimal solutions.

**Why do we need LPP?**

Linear programming is used for obtaining the most optimal solution for a problem with given constraints. In linear programming, we formulate our real-life problem into a mathematical model. It involves an objective function, linear inequalities with subject to constraints.

**Can you have two optimal solutions?**

No, this is not possible. Indeed, consider two optimal solutions and to the optimization problem . A better explanation is that the set of optimal solutions to a convex optimization problem is a convert set itself. Thus, if there are at least 2 optimal solutions, every point between them must also be optimal.