On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
By Andreas Wächter, Lorenz T. Biegler. 2006
10,003 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Numerical Analysis
How researchers see this paper
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In Computational Theory and Mathematics
- Mathias Stolpe
- Fengwen Wang
- Ole Sigmund
- Emmanuel Trélat
- Boyan S. Lazarov
- Enrique Zuazua
- Georg Vossen
- Xiaojin Zhu
In Numerical Analysis
- Wotao Yin
- Ignacio E. Grossmann
- Xiaojin Zhu
- Xuezhou Zhang
- Enrique Zuazua
- Emmanuel Trélat
- Georg Vossen
- Santanu S. Dey
Citations in the corpus
- Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function (2020, methodology): “We use the solver IPOPT [22] in MPC-CBF/MPC-DC/DCLF-DCBF problems [23].”
- Benchmarking optimization solvers for structural topology optimization (2015, methodology): “The interior point methods implemented in IPOPT (Wächter and Biegler 2006) and MATLAB’s FMINCON (Mathworks”
- Large-scale nonlinear programming using IPOPT: An integrating framework for enterprise-wide dynamic optimization (2009, methodology): “The predicted problem is solved in background using IPOPT and the sensitivity system (14) is constructed .”
- Data-driven structural priors for shape completion (2015, methodology): “To update the continuous parameters we use an interior point algorithm [Wächter and Biegler 2006] using numerically estimated gradients, optimizing the following: min ΘB ,ΘA w3ESMD +w4Epair +w5Esymm s.t.”
- An Optimal Control Approach to Sequential Machine Teaching (2018, methodology): “…the NLOptControl package (Febbo, 2017), which is an implementation of the hp-pseudospectral method GPOPS-II (Patterson and Rao, 2014) written in the Julia programming language using the JuMP modeling language (Dunning et al., 2017) and the IPOPT interior-point solver (Wächter and Biegler, 2006).”
- Optimization and uncertainty analysis of ODE models using 2nd order adjoint sensitivity analysis (2018, methodology): “Different variants of this algorithm are implemented in many state-of-the-art optimization toolboxes, like e.g. (Wächter & Biegler, 2006).”
- THEORY AND APPLICATIONS OF OPTIMAL CONTROL PROBLEMS WITH MULTIPLE TIME-DELAYS (2013, neutral): “[33, 34].”
- Inexactness of SDP Relaxation and Valid Inequalities for Optimal Power Flow (2014, methodology): “SDP relaxation is compared against global optimal solution found using BARON [25] and local solution found by MATPOWER [2] and IPOPT [27].”