# HPIPM

> high-performance interior-point method solver

**Wikidata**: [Q134395572](https://www.wikidata.org/wiki/Q134395572)  
**Source**: https://4ort.xyz/entity/hpipm

## Summary
HPIPM is a high-performance interior-point method solver designed for quadratic programming problems, particularly in model predictive control applications. It is an open-source software framework that provides efficient numerical optimization capabilities through specialized linear algebra routines.

## Key Facts
- HPIPM is licensed under the 2-clause BSD License, making it freely available for commercial and non-commercial use
- The software depends on BLASFEO, an open-source linear algebra library, for core computational operations
- HPIPM is described in academic literature as "HPIPM: a high-performance quadratic programming framework for model predictive control"
- The latest stable version is 0.1.3, released on August 13, 2020
- Source code is hosted on GitHub at https://github.com/giaf/hpipm
- HPIPM is classified as software and is copyrighted
- The framework is designed to work with acados, an open-source software framework for nonlinear model predictive control

## FAQs
### Q: What type of problems does HPIPM solve?
A: HPIPM solves quadratic programming problems using interior-point methods, which are particularly useful for optimization tasks in model predictive control systems.

### Q: What license does HPIPM use?
A: HPIPM is distributed under the 2-clause BSD License, which allows for both commercial and non-commercial use with minimal restrictions.

### Q: What are the system requirements for HPIPM?
A: HPIPM requires BLASFEO, an open-source linear algebra library, to perform its core computational operations.

## Why It Matters
HPIPM addresses a critical need in the field of optimization and control systems by providing a high-performance solver for quadratic programming problems. Interior-point methods are essential for solving large-scale optimization problems efficiently, and HPIPM's implementation is specifically tailored for model predictive control applications. This makes it valuable for robotics, autonomous systems, and other fields where real-time optimization is crucial. By leveraging specialized linear algebra routines through BLASFEO, HPIPM achieves performance that can be critical for time-sensitive applications. The open-source nature of the software also allows researchers and engineers to modify and extend the framework for their specific needs, contributing to the broader ecosystem of optimization tools.

## Notable For
- High-performance implementation of interior-point methods specifically optimized for quadratic programming
- Integration with the acados framework for comprehensive model predictive control solutions
- Open-source licensing under the permissive 2-clause BSD License
- Specialized optimization for real-time control applications
- Active development with multiple stable releases since 2019

## Body
### Development and Versions
HPIPM has undergone several stable releases since its initial launch. Version 0.1.0 was released on July 4, 2019, followed by version 0.1.1 on October 1, 2019, and version 0.1.2 on April 7, 2020. The current stable version, 0.1.3, was released on August 13, 2020, representing the most recent development milestone.

### Technical Architecture
The software is built using the Q15777 programming language and relies heavily on BLASFEO for linear algebra operations. This dependency on specialized linear algebra routines is crucial for achieving the high performance that HPIPM is known for. The framework is designed to handle the computational demands of quadratic programming problems efficiently.

### Academic Context
HPIPM is formally described in academic literature as "HPIPM: a high-performance quadratic programming framework for model predictive control," establishing its credibility and purpose within the research community. This academic foundation ensures that the software is built on sound theoretical principles.

### Integration and Ecosystem
The software is designed to work within a broader ecosystem, particularly with acados, an open-source framework for nonlinear model predictive control and moving horizon estimation. This integration capability makes HPIPM a valuable component in comprehensive control system solutions.

### Source and Licensing
The complete source code is available on GitHub at https://github.com/giaf/hpipm, allowing for community contributions and modifications. The 2-clause BSD License provides flexibility for users while maintaining the open-source nature of the project.

## References

1. [Source](https://github.com/giaf/hpipm/blob/master/LICENSE.txt)
2. [Release 0.1.0. 2019](https://github.com/giaf/hpipm/releases/tag/0.1.0)
3. [Release 0.1.1. 2019](https://github.com/giaf/hpipm/releases/tag/0.1.1)
4. [Release 0.1.2. 2020](https://github.com/giaf/hpipm/releases/tag/0.1.2)
5. [Release 0.1.3. 2020](https://github.com/giaf/hpipm/releases/tag/0.1.3)