We’re excited to share some highlights from the EURO Conference in Copenhagen! This year’s event was fantastic, offering great opportunities to connect with the academic elite and industry leaders, share insights, and showcase our latest innovations.
Our booth was buzzing with activity throughout the conference. It was wonderful to meet so many enthusiastic attendees, engage in meaningful conversations, and demonstrate our latest products and services. The positive feedback was truly inspiring, reaffirming our commitment to innovation.
Our team delivered three successful talks, each drawing an interested audience and sparking lively discussions. We covered topics like the Integration of Python in GAMS, Machine Learning and GAMSPy, and Engine SaaS. The response was very positive, with attendees appreciating the depth of knowledge and practical applications discussed. We’re grateful for the opportunity to contribute to our industry’s collective learning and advancement.
Reflecting on our time in Copenhagen, we’re excited for the future. The insights and connections made at the EURO Conference will undoubtedly propel us forward as we continue to innovate and lead in our field.
Thank you to everyone who visited our booth, attended our talks, and engaged with us throughout the conference. We look forward to seeing you at future events and continuing the conversation!
Stay tuned for more updates and innovations from our team. Until next time!
Sign up for our general information newsletter to stay up-to-date!
Our Abstracts
GAMSPy: The Best of Both Worlds - Integrating Python and GAMS
By Justine Broihan
Optimization applications combine technology and expertise from many different areas, including model-building, algorithms, and data-handling. Often, the gathering, pre/post-processing, and visualization of the data is done by a diverse organization-spanning group that shares a common bond: their skill in and appreciation for Python and the vast array of available packages it provides. For this reason, GAMS offers a new comfortable way to integrate with Python on the data-handling and modeling side. In this talk, we will explore the benefits of our Python library GAMSPy.
Integrating Machine Learning with GAMSPy
By Hamdi Burak Usul
GAMSPy is a powerful mathematical optimization package which integrates Python’s flexibility with GAMS’s modeling performance. This combination opens doors to previously challenging applications, notably in bridging the worlds of machine learning (ML) and mathematical modeling. While GAMS excels in indexed algebra, ML predominantly relies on matrix operations. To enable applications in ML, our work introduces essential ML operations such as matrix multiplications, transpositions, and norms into GAMSPy. In this talk, we showcase the use of these additions by generating adversarial images for an optical character recognition network using GAMSPy. We highlight GAMSPy’s versatility and its potential to be used in ML research and development. We delve into future prospects, show how GAMSPy’s approach differs from existing alternatives and discuss innovative methods where mathematical modeling intersects with machine learning.
GAMS Engine SaaS: A Cloud-Based Solution for Large-Scale Optimization Problems
By Frederik Proske
GAMS Engine SaaS is a cloud-based service that allows users to run GAMS jobs on a scalable and flexible infrastructure, currently provided by Amazon Web Services (AWS). It was launched in early 2022 and has since attracted a variety of customers who benefit from its features, such as horizontal auto-scaling, instance sizing, zero maintenance, and simplified license handling. GAMS Engine SaaS is especially suitable for workloads that require large amounts of compute power and can be adapted to many different scenarios. In this presentation, we show a case study of a large international consultant agency that uses GAMS Engine SaaS to run Monte-Carlo simulations of a large energy system model in response to varying climate change scenarios. We describe how they leverage the GAMS Engine API to submit and monitor their jobs, how they select the appropriate instance type for each job, and how they can use custom non-GAMS code on Engine SaaS. We also discuss the challenges and benefits of using GAMS Engine SaaS for this type of application, and provide some insights into the future development of the service.