Tag: optimization
Optimization is an act, process, or methodology of making something as fully perfect or effective as possible. Almost everything can be improved, so optimization’s relevance spans to almost every business or process to make it operate more efficiently and effectively.
Optimization employs mathematical models to discover more efficient ways to control and manage systems, ranging from radiation treatments to data centers and power networks. Optimization researchers at WID solve systems-level problems in emerging science and engineering applications by using optimization technologies in an integrated, interdisciplinary, and collaborative fashion. This includes finding solutions to problems that are the most cost-effective or achieve the highest performance under given constraints by maximizing desired elements and minimizing the undesired elements.
Optimization models promise better process planning that can be tied to and offered by social, economic, and financial systems. Certain social and political constraints have caused optimization to go largely unexplored, as have methods for translating plans into policy. We hope to draw on collaborations with communications experts, political scientists, sociologists, economists, behavioral scientists, and business professionals to further leverage optimization’s potential for boosting efficiencies and improving systems that reach into all corners of our lives.
Learn more about Optimization at WID. It is a key component of WID’s Data Science Hub.
WID Hubs Launch at Illuminating Connections Event
WID’s new hubs—Data Science, Multi-Omics, and Illuminating Discovery—represent a new path forward for collaborative research projects and fields.
New Technology for Controlling Neural Tissue Manufacturing
A paper published in eLife this week by an interdisciplinary team at WID describes new methods for reproducibly manufacturing brain and spinal cord organoids with strict control over morphogenic and developmental processes.
WID Researchers Showcase “Virtual Dairy Farm Brain” at American Dairy Science Association Meeting
By combining information from many farms, predictive models and analytic tools can be developed to help producers and consultants navigate, visualize. and analyze the data they are getting from an increasing number of sources to support better management decisions.
Using Data Science to Find the Next El Nino
A new approach to climate data analysis hopes to improve regional forecasts.
WID Researchers Looking to the Future with UW2020 Awards
Investigators from WID are among the recipients of the latest round of UW2020 awards.
Applying Control Theory to Algorithm Design Earns Laurent Lessard an NSF CAREER Award
Laurent Lessard is working to improve the algorithms and computer software that keep the modern world running smoothly.
When Communicating with Color, Balance Can Be a Path to Accuracy
Karen Schloss and Laurent Lessard are working on a method for matching colors to people’s expectations to send the right message — starting with the best colors for waste and recycling bins.
Interdisciplinary Faculty Build Data Science Future in New Institute Based at WID
The new institute, housed at UW–Madison’s Wisconsin Institute for Discovery (WID), will play a key role in the future of data science, developing fundamental techniques for handling increasingly massive data sets in shorter times.
Jeff Linderoth
Professor and Department Chair
Models and Algorithms for Large-Scale Numerical Optimization
Wright Research Group
Numerical optimization, especially problems involving real (as opposed to integer or discrete) variables. Includes theory, algorithms, implementation and application.
Nowak Research Group
The NRG focuses on signal processing, machine learning, optimization, and statistics. Areas of focus include sparsity and active learning, learning graphs and networks, and interactive machine learning with humans.
Rutherford Research Group
Interests include energy markets, Climate Policy, international trade, technical change and computational economics.
Del Pia Research Group
Theoretical and algorithmic aspects of mixed-integer optimization, with a special emphasis in linear and polynomial functions. Other interests include polyhedral combinatorics and combinatorial optimization.
Ferris Research Group
Algorithmic and interface development for large scale problems in mathematical programming, including links to the GAMS and AMPL modeling languages, and general purpose software such as PATH, NLPEC and FATCOP.
Alberto Del Pia
Associate Professor
Design of exact and approximate algorithms for mixed-integer optimization problems
Jim Luedtke
Professor
Design of methods for solving discrete and stochastic optimization problems.