# Constraints

Verteego supports a versatile set of constraints that can be applied to optimize various business operations effectively. These constraints ensure that the solutions provided by the platform not only achieve the desired outcomes but also adhere to all specified operational limits and conditions.

### Types of Constraints Supported

[**Unary Constraint**](/verteego-doc/pipelines/optimization-pipelines/constraints/unary-constraint.md)

* **Description:** A Unary Constraint applies a single condition to one variable within the dataset. This type of constraint is typically used to set limits (such as minimum or maximum values) on individual variables.
* **Use Case:** Ensuring that the quantity of a product does not exceed warehouse capacity.

[**Binary Constraint**](/verteego-doc/pipelines/optimization-pipelines/constraints/binary-constraint.md)

* **Description:** Binary Constraints involve relationships between two variables. These are used to establish a direct comparison or interaction, such as equality or inequality.
* **Use Case:** Linking sales volume to stock levels to avoid overproduction.

[**Aggregation Constraint**](/verteego-doc/pipelines/optimization-pipelines/constraints/aggregation-constraint.md)

* **Description:** This constraint deals with aggregated data across a set of variables. It is useful for conditions that involve sums, averages, or other aggregate calculations.
* **Use Case:** Maintaining budget limits by capping the total expenditure across multiple departments.

[**Order Constraint**](/verteego-doc/pipelines/optimization-pipelines/constraints/order-constraint.md)

* **Description:** An Order Constraint imposes an order among the values of different variables, ensuring that they follow a specified sequence or hierarchy.
* **Use Case:** Sequencing of project tasks where certain tasks must logically precede others.

[**Multiplicative Equality Constraint**](/verteego-doc/pipelines/optimization-pipelines/constraints/multiplicative-equality-constraint.md)

* **Description:** These constraints involve equalities that include multiplicative terms of variables. They are complex and used to model interactions where variables are dependent on the product of others.
* **Use Case:** Adjusting resource allocation proportionally based on varying demand levels.

### Dynamic Constraint Generation from Datasets

Verteego provides the capability to dynamically generate constraints based on real-time or historical data, enhancing the flexibility and applicability of the optimization process:

* **Functionality:** Users can specify rules and conditions directly through the interface, choosing relevant columns and defining how these should translate into constraints.
* **Advantages:** This feature allows constraints to be adaptive to changes in data, ensuring that the optimization process remains relevant and effective under varying business conditions and data states.

[Learn how to generate constraints from Datasets >>](/verteego-doc/pipelines/optimization-pipelines/constraints/generate-constraints-from-a-dataset.md)

<br>


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://doc.verteego.com/verteego-doc/pipelines/optimization-pipelines/constraints.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.