qpOASES: a parametric active-set algorithm for quadratic programming

By Hans Joachim Ferreau, Christian Kirches, Andreas Potschka, Hans Georg Bock, Moritz Diehl. Mathematical Programming Computation, 2014

1,337 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/ca3a7e977f1ca6edc5b39483c5b1d7f7b0b7d02e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Steven Diamond
2. Milan Korda
3. Akshay Agrawal
4. Robin Verschueren
5. Moritz Diehl
6. Rien Quirynen
7. Alberto Bemporad
8. Stephen P. Boyd

In Computational Theory and Mathematics

1. Steven Diamond
2. Stephen P. Boyd
3. Alberto Bemporad
4. Akshay Agrawal
5. Moritz Diehl
6. Robin Verschueren
7. Gianluca Frison
8. Rien Quirynen

In Numerical Analysis

1. Alberto Bemporad
2. Steven Diamond
3. Stephen P. Boyd
4. Akshay Agrawal
5. Gianluca Frison
6. Moritz Diehl
7. Robin Verschueren
8. Benjamin Katz

Citations in the corpus

• Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control (2016, methodology): “(9)The optimization problems were solved by qpOASES [6] running on Matlab and 2 GHz Intel Core i7 with 8 GB RAM.”
• HPIPM: a high-performance quadratic programming framework for model predictive control (2020, methodology): “Furthermore, the (partial) condensing routines provided by HPIPM are used as preprocessing step in the solution of these QPs by means of other QP solvers such as qpOASES (Ferreau et al., 2014), qpDUNES (Frasch et al.”
• A Rewriting System for Convex Optimization Problems (2017, background): “Examples of solvers specialized to each class include GLPK (Makhorin, 2016) and CBC (Forrest & Lougee-Heimer, 2005) for linear programs; qpOASES (Ferreau, Kirches, Potschka, Bock, & Diehl, 2014) and OSQP (Banjac, Goulart, Stellato, & Boyd, 2017) for quadratic programs; ECOS (Dom-ahidi, Chu, & Boyd,…”
• Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control (2018, methodology): “The software uses the Eigen3 linear algebra library [30] and the qpOASES [24] quadratic programming library.”
• Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges (2020, background): “A solver that has been gaining popularity in the power electronics community is qpOASES [187].”
• From linear to nonlinear MPC: bridging the gap via the real-time iteration (2020, positive): “…cantly sped up computational times, allowing for the deployment of MPC at outstanding speeds (Domahidi, Zgraggen, Zeilinger, Morari, & Jones, 2012; Ferreau, Kirches, Potschka, Bock, & Diehl, 2014; Frasch, Sager, & Diehl, 2015; Frison, Sorensen, Dammann, & Jorgensen, 2014; Patrinos & Bemporad,…”