## What is lpSolve package?

The lpSolve R package is the first implementation of an interface of lpsolve to R. It provides high-level functions for solving general linear/integer problems, assignment problems and transportation problems. The following link contains the version of the driver: lpSolve: Interface to Lp_solve v.

**What is lpSolve IDE?**

The LPSolve IDE (Integrated Development Interface) is a very user friendly Windows interface to the lpsolve API. All functionality of lpsolve can be accessed via a graphical and very user friendly application.

### What is lpSolve package in R?

The lpSolve R package allows to solve linear programming problems and get significant statistical information (i.e. sensitivity analysis) with just a few lines of code.

**How do you solve a linear programming problem using graphical method?**

The Graphical Method

- Step 1: Formulate the LP (Linear programming) problem.
- Step 2: Construct a graph and plot the constraint lines.
- Step 3: Determine the valid side of each constraint line.
- Step 4: Identify the feasible solution region.
- Step 5: Plot the objective function on the graph.
- Step 6: Find the optimum point.

#### Why do we need to use an LP solver?

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.

**How do you write a linear programming problem?**

The process to formulate a Linear Programming problem

- Identify the decision variables.
- Write the objective function.
- Mention the constraints.
- Explicitly state the non-negativity restriction.

## How do you create a linear programming problem?

Steps to Linear Programming

- Understand the problem.
- Describe the objective.
- Define the decision variables.
- Write the objective function.
- Describe the constraints.
- Write the constraints in terms of the decision variables.
- Add the nonnegativity constraints.
- Maximize.

**Is linear programming NP complete?**

Integer Linear Programming is known as NP-complete problem, but non-integer Linear Programming problems can be solved in polynomial time, what places them in P class. Index Termsâ€”complexity class, linear programming, P vs NP, large instances.