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National University of Singapore
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Video
Convex Matrix Optimization (MOP) arises in a wide variety of applications. The last three decades have seen dramatic advances in the theory and practice of matrix optimization because of its extremely powerful modeling capability. In particular, semidefinite programming (SP) and its generalizations have been widely used to model problems in applications such as combinatorial and polynomial optimization, covariance matrix estimation, matrix completion and sensor network localization. The first part of the talk will describe the primal-dual interior-point methods (IPMs) implemented in SDPT3 for solving medium scale SP, followed by inexact IPMs (with linear systems solved by iterative solvers) for large scale SDP and discussions on their inherent limitations. The second part will present algorithmic advances for solving large scale SDP based on the proximal-point or augmented Lagrangian framework In particular, we describe the design and implementation of an augmented Lagrangian based method (called SDPNAL+) for solving SDP problems with large number of linear constraints. The last part of the talk will focus on recent advances on using a combination of local search methods and convex lifting to solve low-rank factorization models of SP problems.
Event date: 11/10/2022
Speaker: Prof. Kim-Chuan Toh (National University of Singapore)
Hosted by: Department of Applied Mathematics
- Subjects:
- Mathematics and Statistics
- Keywords:
- Semidefinite programming Convex programming
- Resource Type:
- Video
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MOOC
The building industry is exploding with data sources that impact the energy performance of the built environment and health and well-being of occupants. Spreadsheets just don’t cut it anymore as the sole analytics tool for professionals in this field. Participating in mainstream data science courses might provide skills such as programming and statistics, however the applied context to buildings is missing, which is the most important part for beginners. This course focuses on the development of data science skills for professionals specifically in the built environment sector. It targets architects, engineers, construction and facilities managers with little or no previous programming experience. An introduction to data science skills is given in the context of the building life cycle phases. Participants will use large, open data sets from the design, construction, and operations of buildings to learn and practice data science techniques. Essentially this course is designed to add new tools and skills to supplement spreadsheets. Major technical topics include data loading, processing, visualization, and basic machine learning using the Python programming language, the Pandas data analytics and sci-kit learn machine learning libraries, and the web-based Colaboratory environment. In addition, the course will provide numerous learning paths for various built environment-related tasks to facilitate further growth.
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Python (Computer program language) City planning -- Statistical methods Information visualization
- Resource Type:
- MOOC