Alberto De Marchi

Dr.rer.nat., Applied Mathematics, UniBw M, Munich, DE
M.Sc. in Mechatronics Engineering, UniTn, Trento, IT
B.Sc. in Industrial Engineering, UniTn, Trento, IT

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About me

I am a Research Associate at the Institute for Applied Mathematics and Scientific Computing at UniBw M, Germany. My scientific activity revolves around computational optimization, with applications ranging from control systems to data science.

In Fall 2022, I was a Visiting Research Associate at Curtin University, Australia. Previously, I obtained my doctoral degree from UniBw M (2021), advised by Matthias Gerdts. I received a MSc in Mechatronics Engineering (2016) and a BSc in Industrial Engineering (2014) from UniTn. I grew up in the Venetian countryside, Italy, between the Mediterranean Sea and the Dolomites.

🌱 I enjoy reading about the problem of mind and I am curious about how people came to be curious. Asimov's Foundation, Tolstoy's War and Peace, and Andrić's The Bridge on the Drina are among my favourite books.


Think twice, code once, compute once.

A sustainable future demands efficient and scalable methods for decision-making and problem-solving. Computational optimization can help us thrive along with our Planet.

🔭 I focus on analysing mathematical models and developing numerical tools that are general, robust, and yet simple. By exploiting problems' structures, we can design and deploy widely applicable, reliable, and fast algorithms for our daily life. For me, this means walking on the edge of bizarre problem classes to better grasp their essence.

I am fascinated by the interplay between numerical optimization, linear algebra, and the underlying computing system. Theoretical aspects, numerical properties, algorithmic design, and practical implementations are all part of my investigations.

I have the pleasure of sharing this journey with great collaborators, colleagues, and friends, all over the world. Recently, I have been working on

↪ stabilized, primal-dual Newton-type methods for nonlinear programming;
↪ proximal gradient algorithms for structured optimization;
↪ Lagrangian and proximal methods for generalized programming;
↪ bridging the gap between discrete and continuous optimization.