9. Optimization Techniques for Sparse and Structured Solutions in Big Data
Invited abstract in session MB-2: Optimization and applications, stream Nonsmooth and nonconvex optimization.
Monday, 10:30-12:30Room: B100/7011
Authors (first author is the speaker)
| 1. | Maurine Wafula
|
| Data Science, USIU-Africa | |
| 2. | Leah Mutanu
|
Abstract
In recent years, the explosion of data has created both incredible opportunities and significant challenges for the field of data analysis. To effectively analyze massive datasets, we need optimization techniques that are not only efficient but also help us understand the underlying patterns. This paper explores a powerful approach: focusing on solutions that are both sparse where, they only use a small subset of the available information and structured, reflecting the inherent organization of the data. The research will look into the theoretical foundations of these techniques, showcasing how they can be used to identify the most important features in a dataset while minimizing noise and irrelevant information. We then introduce novel optimization algorithms specifically designed for large-scale problems, ensuring they can handle the sheer volume of data we face today. These algorithms are not just about speed; they also guarantee reliable results and can be adapted to work on constantly evolving datasets. The research will demonstrate the practical value of these techniques through real-world applications in fields like machine learning, image processing, and bioinformatics. For example, in medicine, these methods can help identify crucial genetic markers associated with diseases, paving the way for more personalized treatments. This research bridges the gap between theoretical advancements and practical needs. By carefully balancing accuracy, computational efficiency, and
Keywords
- Distributed optimization
- Optimization for learning and data analysis
- Optimization for learning and data analysis
Status: accepted
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