Siegfried Nijssen’s Post

I'm thrilled to share our latest research: "MUSE-BB: A Decomposition Algorithm for Nonconvex Two-Stage Problems using Strong Multisection Branching", which has been submitted to the Journal of Global Optimization. For those interested, a preprint is available at Optimization Online: https://lnkd.in/dpdY6_gh In this work, we present a branch and bound based decomposition algorithm for deterministic global optimization of nonconvex two-stage stochastic programming problems. This problem class is highly relevant for decision-making under uncertainty, e.g., in the design and operation of energy and process systems. Our implementation of MUSE-BB will soon be available in the next release of our open-source deterministic global optimization solver MAiNGO 🥭 https://lnkd.in/dnXm2FJg Thanks to my coauthors Manuel Dahmen, Dominik Bongartz and Alexander Mitsos. #Optimization #StochasticProgramming #GlobalOptimization #DecompositionAlgorithm #NonconvexProblems #openSource

What started as an assignment for my Structural Dynamics (CE-809) course has turned into a report, now available as a preprint titled 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿 𝗼𝗳 𝗦𝗗𝗢𝗙 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: 𝗔 𝗠𝗔𝗧𝗟𝗔𝗕-𝗕𝗮𝘀𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀. You can access it on Research Square: https://lnkd.in/d5sdEkkD. This report examines the dynamic response of single-degree-of-freedom (SDOF) systems under various conditions using MATLAB. Key areas of investigation include: • Comparing undamped and damped systems (ζ < 1, ζ = 1, ζ > 1). • Assessing the effects of mass and stiffness on system dynamics. • Analyzing dynamic responses with increased mass and varying damping ratios.

The 3SR laboratory is proud to inform you that the free software SPAM - Software for the Practical Analysis of Materials, initiated and co-developed at 3SR, was awarded an ‘Open Science Prize for Free Software in Research 2024’ in the << Coup de coeur>> category’, a prize awarded yesterday in Marseille-France, under the auspices of the French Ministry of Higher Education and Research, https://lnkd.in/gApugE7A SPAM is a software package for the quantitative analysis of data from 2D and 3D imaging applied to mechanics, in particular for X-ray and neutron tomography. https://lnkd.in/grVtdiTe

🚀 Just published: In-Depth Guide to Image Descriptors: LBP, HOG, and Gabor Filters in Computer Vision. 🚀 The full article is now live on Medium! In this piece, I dive into LBP, HOG, and Gabor Filters, explaining how each method works and demonstrating their application to images with practical Python examples. You’ll see how each technique captures distinct features, helping you understand their strengths and use cases in computer vision tasks. If you're curious about how these methods perform on images and want to see the results in action, check out the article! Feel free to share your thoughts or ask questions! 😁 #ComputerVision #AI #MachineLearning #DataScience #OpenCV #ImageDescriptors #Python #LBP #HOG #GaborFilters

Bayesian evidence calculation for model selection in 150+ dimensions. Check out our latest paper to see how: https://lnkd.in/eqSbXuBe We advocate for a new paradigm for likelihood-based Bayesian inference combining emulation, differentiable and probabilistic programming, scalable gradient-based MCMC sampling, and decoupled and scalable techniques that compute the Bayesian evidence purely from posterior samples. Key to this is the CosmoPower-JAX and harmonic codes. CosmoPower: https://lnkd.in/eQc-6QDk CosmoPower-JAX: https://lnkd.in/et9VUGyN harmonic: https://lnkd.in/ekh3FHuT By the amazing Davide Piras, Alicja Polanska, Alessio Spurio Mancini, Matthew Price.

As part of our discussion on predictive coding, we identified that many experiments have created a diversity of error types: Temporal sequences errors, visual oddballs, sensory-motor mismatches... A key assumption is that all of those errors are leveraged by a similar cortical circuit mechanism: Prediction comes in Layer I and is compared with some bottom-up inputs. Most experimental papers then proceed to test different ideas on how local neurons handle this information. But is this working assumption true? Have we shown experimentally that all of those sensory errors are handled by the same mechanism? Is it a safe theoretical assumption? To dive in more details : https://lnkd.in/gXSQmbFz

🔥 It's out our #Quantum Matrix Multiplication Algorithm! 🔍 Why might you be interested? 💙 - Matrix multiplication is transversal to so many fields. 💙 - The overall #depth of the algorithm is #polylogarithmically with matrix dimensions! 💙 - The output entries of the multiplication are encoded in the final state vector. That's useful for computing non-homomorphic functions (for example!) 💡Do you have any interesting applications to discuss? 🙏🏻Many thanks to Anna Bernasconi, Gianna M. Del Corso, and Alessandro Poggiali 👇🏻You can find the full article in the first comment below!

🚀 Introducing the #Quantum Matrix Multiplication Algorithm by Alessandro Berti! 🚀 🔍 What's new? 💙 Universality: Matrix multiplication is a cornerstone operation across numerous fields, from data science and machine learning to engineering and physics. 💙 Efficiency: This algorithm achieves polylogarithmic depth relative to matrix dimensions, dramatically enhancing computational speed and scalability. 💙 Innovative Encoding: The output entries are encoded in the final state vector, enabling the computation of non-homomorphic functions and opening doors to new quantum applications! 💡 Do You Have Any Interesting Applications to Discuss? Comment below. You Can Find the Full Article: https://lnkd.in/gVYYpJkQ Follow this page to stay updated on the latest advancements in quantum computing and cutting-edge algorithms! #QuantumComputing #MatrixMultiplication #QuantumAlgorithm #Innovation #Tech #QuantumTech #Research #FutureOfComputing #QuantumInnovation

1927 Marguerite Straus Frank (born September 8, 1927) is a French-American mathematician who is a pioneer in convex optimization theory and mathematical programming.   She is famed both for her remarkable new discoveries of simple Lie algebras, and her solution to the problem of maximizing a concave quadratic function, now known as the Frank-Wolfe algorithm. This algorithm is used widely in traffic models to assign routes to strategic models such as those using Saturn (software). #onthisdayinmath #womeninscience

Please checkout my post on Graph Theory Algorithms