We discuss state-of-the-art in mixed-integer nonlinear optimization (MINLP) solver software and the current state of research in the field. We hope to spark a conversation about the connections to NMPC.

Numerical approaches to mixed-integer nonlinear optimization

**Session Chair:** Eric C. Kerrigan

**Author:** Misener Ruth
**Paper link:** Please find my paper **[HERE](https://drive.google.com/file/d/1D_6bzeR7hRzfj0IYfpL06t94EaQzADkg/view?usp=sharing)**.

![](https://assets.underline.io/uploads/markdown_image/1/image/3d9f1542c1df541084cfff0ddd4f662a.png)

**Biography**:
 Dr Ruth Misener (http://wp.doc.ic.ac.uk/rmisener/) is a Professor in Computational Optimization in the Imperial College London Department of Computing. Ruth received an SB from MIT and a PhD from Princeton: both degrees are in chemical engineering. Foundations of her research are in numerical optimization algorithms. Applications include bioprocess optimization under uncertainty, petrochemical process design and operations, and explainable scheduling. Ruth's research team makes their software contributions available open source (https://github.com/cog-imperial). Ruth received the 2017 Macfarlane Medal from the Royal Academy of Engineering and the 2020 Outstanding Young Researcher Award from the AIChE Computing & Systems Technology Division. Ruth is an associate editor for Computers & Chemical Engineering and INFORMS Journal on Computing.


**Abstract**:
 We discuss state-of-the-art in mixed-integer nonlinear optimization (MINLP) solver software and the current state of research in the field. We hope to spark a conversation about the connections to NMPC.

Plenary Session 4: Numerical approaches to mixed-integer nonlinear optimization 

invited talk

The IFAC Conference on Nonlinear Model Predictive Control (NMPC 2021) aims to bring together researchers interested in and work in the MPC field from both academia and industry. This allows us to reflect and establish the current state-of-the-art and focus on the future development of the MPC field towards relevant directions.

Model predictive control (MPC) is indisputably one of the advanced control techniques that have significantly affected control engineering practice with thousands of controllers implemented in various fields, spanning from the process industry to automotive and robotics. This is due to its unique ability to handle constraints, take preview information about disturbances and setpoint changes into account, and optimize performance. Furthermore, MPC allows the consideration of various types of dynamics, from linear to nonlinear, discrete to continuous, as well as an online adaptation of models and parameters. The research community on MPC is very active, with hundreds of papers published every year. Over the past decades, many theoretical, as well as implementation results, have been achieved.

### **All content presented at NMPC2021, including videos, papers and slides will be available only to registered (regular/student) participants. The content can be accessed until August 31st, 2021.**



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Nonlinear Model Predictive Control Conference 2021

The IFAC Conference on Nonlinear Model Predictive Control (NMPC 2021) aims to bring together researchers interested in and work in the MPC field from both academia and industry. This allows us to reflect and establish the current state-of-the-art and focus on the future development of the MPC field towards relevant directions.

Ruth Misener

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Numerical approaches to mixed-integer nonlinear optimization

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