Panua Technologies develops customized high-end software solutions for large-scale prediction, simulation, optimization, and graph analytics.

Panua - Ipopt

The powerful nonlinear solver, with Panua-Pardiso integrated in it, offering a wide range of performance and robustness improvements.

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Panua - Pardiso

The high-performance software for the solution of large sparse symmetric and unsymmetric linear systems of equations.

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Panua Technologies

is a Swiss software company headquartered in Lugano, Switzerland that creates customized high-end software solutions for large-scale prediction, simulation, optimization, and graph analytics. Panua is a spin-off from the Faculty of Informatics at Università della Svizzera italiana. It develops highly flexible software platforms that support various aspects of computational optimization, applied in business-critical problems. Panua bridges the realms of academic algorithmic research and industrial software development to provide accurate and computationally performant graph and data analysis.

Our Products

Panua - Ipopt

Ipopt - is an especially powerful nonlinear solver, offering a range of state-of-the-art algorithms and options for working with smooth objective and constraint functions in continuous variables. It is designed for local optimization of large-scale problems with up to million of variables. The integration of Panua-Pardiso offers a wide range of performance and robustness improvements.

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Panua - Pardiso

The package Pardiso is a thread-safe, high-performance, robust, memory efficient, and easy to use software for solving large sparse symmetric and unsymmetric linear systems of equations on shared-memory and distributed-memory multiprocessors, and on Intel and ARM architectures.

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Our Team

Andreas Wächter picture

Andreas Wächter

PhD, Director at Panua Technologies

Andreas Wächter is a Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University. He obtained his master's degree in Mathematics at the University of Cologne, Germany, and his Ph.D. in Chemical Engineering at Carnegie Mellon University. Before joining Northwestern University, he was a Research Staff Member in the Department of Mathematical Sciences at IBM Research in Yorktown Heights, NY. His research interests include the design, analysis, implementation and application of numerical algorithms for nonlinear continuous optimization and application to industrial and scientific problems. He is a recipient of the 2011 Wilkinson Prize for Numerical Software and the 2009 INFORMS Computing Society Prize for his work on the optimization software. Andreas Wächter is a Fellow of the Society for Industrial and Applied Mathematics (SIAM).

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Olaf Schenk

PhD, Founder, Director at Panua Technologies

Olaf Schenk is the founder of Panua Technologies. He is a professor at the Institute of Computing at the Faculty of Informatics at Universita della Svizzera italiana (USI), Switzerland, and an adjunct member of the Computer Systems Institute at USI. He is the co-director of the Institute of Computing and of the Master of Science in Computational Science at USI. He graduated in Applied Mathematics from Karlsruhe Institute of Technology (KIT), Germany, earned his PhD from the Department of Information Technology and Electrical Engineering of the Swiss Federal Institute of Technology (ETH) in Zurich, and a venia legendi from the Department of Mathematics and Computer Science from the University of Basel. Olaf Schenk is an elected Fellow of the Society of Industrial and Applied Mathematics (SIAM). His research interests are inherent to the field of high-performance computing, in particular to the development and optimization of algorithms and software tools to perform large-scale simulations.

Waldemar Kubli picture

Waldemar Kubli

PhD, Advisor of Panua Technologies

Waldemar Kubli is the founder and chief product officer of the AutoForm Group. He holds a doctorate in mechanical engineering and a master's in business administration. In the early 1990s, Dr. Kubli directed a project at the Institute of Metal Forming at the Swiss Federal Institute of Technology (ETH) in Zurich to build a prototype of a new solver for automotive forming simulation. Dr. Kubli created the AutoForm Group in 1995 and served as its chief executive officer. Due to the tremendous success of AutoForm, he was elected Entrepreneur of the Year in 2002. Autoform has become one of the most successful ETH spin-offs companies and was aquired for a record price by an American investment firm in 2022. In his capacity as an advisor for Panua Technologies, he provides direction and advice regarding future initiatives and corporate partnerships.

Manufacturers and Suppliers using Panua Software

Newsletter

Panua Software used in Grid Optimization Competition Challenge

Panua Technologies is among the sponsors of the Grid Optimization Competition Challenge organized by the Department of Energy (DOE) and both Panua-Pardiso and Panua-Ipopt are available to participants.

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On cheap entropy-sparsified regression learning

The PNAS paper "On cheap entropy-sparsified regression learning" presents a method for training regression models that is more efficient and effective than previous machine learning approaches. The method, called entropy-sparsified regression learning, uses the algorithm Ipopt in the Panua optimization framework to solve a series of optimization problems. By doing so, it is able to learn complex models with high accuracy while using fewer resources and requiring less data than traditional methods. The paper demonstrates the effectiveness of this method on several real-world datasets, showing that it can be used to improve the performance of machine learning models in a variety of applications. The optimization framework Panua-Ipopt shows promise for increasing efficiency and accuracy in machine learning applications. I. Horenko, E. Vecchi , J. Kardoš, O. Schenk, A. Waechter, T. O’Kane, P. Gagliardini, S. Gerber, On cheap entropy-sparsified regression learning Proceedings of the National Academy of Sciences (PNAS), November 2022, pages 1-12, https://www.pnas.org/doi/10.1073/pnas.2214972120 .

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