# Mathematical Modeling¶

This section provides an introduction to Pyomo: Python Optimization Modeling Objects. A more complete description is contained in the [PyomoBookII] book. Pyomo supports the formulation and analysis of mathematical models for complex optimization applications. This capability is commonly associated with commercially available algebraic modeling languages (AMLs) such as [AMPL], [AIMMS], and [GAMS]. Pyomo’s modeling objects are embedded within Python, a full-featured, high-level programming language that contains a rich set of supporting libraries.

Modeling is a fundamental process in many aspects of scientific research, engineering and business. Modeling involves the formulation of a simplified representation of a system or real-world object. Thus, modeling tools like Pyomo can be used in a variety of ways:

• Explain phenomena that arise in a system,
• Make predictions about future states of a system,
• Assess key factors that influence phenomena in a system,
• Identify extreme states in a system, that might represent worst-case scenarios or minimal cost plans, and
• Analyze trade-offs to support human decision makers.

Mathematical models represent system knowledge with a formalized language. The following mathematical concepts are central to modern modeling activities:

## Variables¶

Variables represent unknown or changing parts of a model (e.g., whether or not to make a decision, or the characteristic of a system outcome). The values taken by the variables are often referred to as a solution and are usually an output of the optimization process.

## Parameters¶

Parameters represents the data that must be supplied to perform the optimization. In fact, in some settings the word data is used in place of the word parameters.

## Relations¶

These are equations, inequalities or other mathematical relationships that define how different parts of a model are connected to each other.

## Goals¶

These are functions that reflect goals and objectives for the system being modeled.

The widespread availability of computing resources has made the numerical analysis of mathematical models a commonplace activity. Without a modeling language, the process of setting up input files, executing a solver and extracting the final results from the solver output is tedious and error-prone. This difficulty is compounded in complex, large-scale real-world applications which are difficult to debug when errors occur. Additionally, there are many different formats used by optimization software packages, and few formats are recognized by many optimizers. Thus the application of multiple optimization solvers to analyze a model introduces additional complexities.

Pyomo is an AML that extends Python to include objects for mathematical modeling. [PyomoBookI], [PyomoBookII], and [PyomoJournal] compare Pyomo with other AMLs. Although many good AMLs have been developed for optimization models, the following are motivating factors for the development of Pyomo:

• Open Source

Pyomo is developed within Pyomo’s open source project to promote transparency of the modeling framework and encourage community development of Pyomo capabilities.

• Customizable Capability

Pyomo supports a customizable capability through the extensive use of plug-ins to modularize software components.

• Solver Integration

Pyomo models can be optimized with solvers that are written either in Python or in compiled, low-level languages.

• Programming Language

Pyomo leverages a high-level programming language, which has several advantages over custom AMLs: a very robust language, extensive documentation, a rich set of standard libraries, support for modern programming features like classes and functions, and portability to many platforms.