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About me
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Short description of portfolio item number 1
Short description of portfolio item number 2
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf
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This seminar is about a recursive framework for improving convergence performance in expectation on convex stochastic optimization. By replacing the gradient of the reference point with the last iterate, the stochastic average gradient algorithm (SAGA) saves more computational resource with linear convergence, and supports for composite objectives where a proximal operator is used on the regularizer, compared with stochastic variance reduced gradients (SVRG).
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In this seminar, I first introduced the smooth convex-concave saddle point problem and its intuitions. To solve such problem, I intuitively showed an algorithm called gradient descent-ascent (GDA) that theoretically feasible but practically diverged, and further showing its converged variant, proximal point algorithm (PPA). Given the intractability of PPA’s future step gradient $\nabla f(x_{k+1},y_{k+1})$, I provided the optimistic gradient descent-ascent algorithm (OGDA) and the extragradient (EG) algorithm, and highlighted how gradients used in OGDA and EG approximate the gradient of the PPA. Then, I exploit this interpretation to show that the primal-dual gap of the averaged iterates generated by both algorithms converge with a rate of $O(1/k)$. Ultimately, I analyzed the last iterate convergence properties of both algorithms, and showed that the last iterate of both algorithms converge at a rate of $O(1/\sqrt{k})$, which is slower than the averaged iterate in smooth convex-concave saddle point problem.
Instructor, School of Business, Macau University of Science and Technology, 2019
The Global Management Challenge (GMC) is a global strategic operations management competition that runs with a complex computer simulation system, in which each team runs different virtual company in the same market environment, and competes by developing and producing products that can better meet customer needs to maximize their investment performance. Since 2019, I have become the instructor for this workshop and take in charge of the graduate and undergraduate instruction for two semesters (2019 & 2020 Fall).