Introducing the quadratically-constrained quadratic programming framework in HPIPM

TL;DR

This paper presents a new quadratically-constrained quadratic programming (QCQP) framework in HPIPM, enabling efficient and reliable solutions for more general optimal control problems with features matching the existing QP framework.

Contribution

The paper introduces a QCQP framework in HPIPM that extends the existing QP capabilities, supporting multiple QCQP types and maintaining high performance and reliability.

Findings

Developed a QCQP framework with full feature parity to QP

Implemented fast interior point method (IPM) solvers for QCQPs

Supported various QCQP problem structures including dense and tree-structured

Abstract

This paper introduces the quadratically-constrained quadratic programming (QCQP) framework recently added in HPIPM alongside the original quadratic-programming (QP) framework. The aim of the new framework is unchanged, namely providing the building blocks to efficiently and reliably solve (more general classes of) optimal control problems (OCP). The newly introduced QCQP framework provides full features parity with the original QP framework: three types of QCQPs (dense, optimal control and tree-structured optimal control QCQPs) and interior point method (IPM) solvers as well as (partial) condensing and other pre-processing routines. Leveraging the modular structure of HPIPM, the new QCQP framework builds on the QP building blocks and similarly provides fast and reliable IPM solvers.