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Optimization methods in applications II

INTENDED LEARNING OUTCOME

At the end of the course, the students will have an understanding of modern algorithms in nonlinear optimization. They will know their applications from several fields and they will understand which method is suitable for which application.


CONTENT

The course consists of two parts. The first one provides an overview of modern optimization methods while the second one shows selected applications. The first part will be theoretical and the emphasis will be given on explaining how different optimization approaches work. Their strengths and weaknesses will be properly analyzed. The second part will use this knowledge and show how these methods can be applied to modern machine learning applications or topology optimization.


PART TWO (APPLICATIONS)

6- Multi-objective optimization (Pareto optimality, scalarization, eps-approach, their relation, selected algorithms)
Time: Monday, Oct 14, 2019, 8:30-10:00
Venue: Room 518, Block 3, Hui Yuan
7- Machine learning (linear and logistic regression, closed-form solution, support vector machines, dualization, kernelization, sparsity recovery)
Time: Tuesday, Oct 15, 2019, 8:30-10:00
Venue: Room 415, Block 3, Hui Yuan
8- Machine learning (neural networks, computation of derivatives, stochastic gradient descent, faster optimizers)
Time: Wednesday, Oct 16, 2019, 8:30-10:00
Venue: Room 415, Block 3, Hui Yuan
9- Topology optimization (differences with sizing optimization, specific properties, discretization, mesh independence, cantilever optimization)
Time: Thursday, Oct 17, 2019, 8:30-10:00
Venue: Room 518, Block 3, Hui Yuan
10- Topology optimization (complications, numerical artefacts and how to evade them, optimization of micro-structure, robust optimization)
Time: Friday, Oct 18, 2019, 8:30-10:00

Venue: Room 415, Block 3, Hui Yuan


RECOMMENDED LITERATURE
- Nocedal, Jorge, and Stephen Wright. Numerical optimization. Springer Science & Business Media, 2006.
- Hastie, Trevor, et al. The elements of statistical learning: data mining, inference and prediction. Springer, 2005.

- Bends, Martin P. Topology optimization. Springer US, 2009.


BIOGRAPHY

Dr. Lukas Adam, 2015年毕业于Charles University in Prague,Czech Republic,获数学博士学位。师从国际著名优化专家J Outrata. 现任南方科技大学计算机科学系研究助理教授。在SIAM Journal on Applied Mathematics ,Mathematical Programming等应用数学重要杂志上发表多篇有影响力论文。他的研究兴趣涵盖最优化理论、方法以及在数据科学中的应用。