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Person# Quoc Tran Dinh

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Convex optimization

Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets (or, equivalently, maximizing concave functions over convex sets

Algorithm

In mathematics and computer science, an algorithm (ˈælɡərɪðəm) is a finite sequence of rigorous instructions, typically used to solve a class of specific problems or to perform a computation. Algo

Problem solving

Problem solving is the process of achieving a goal by overcoming obstacles, a frequent part of most activities. Problems in need of solutions range from simple personal tasks (e.g. how to turn on an a

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Volkan Cevher, Sandip De, Junhong Lin, Quoc Tran Dinh

Machine learning promises to accelerate materials discovery by allowing computational efficient property predictions from a small number of reference calculations. As a result, the literature has spent a considerable effort in designing representations that capture basic physical properties. Our work focuses on the less-studied learning formulations in this context in order to exploit inner structures in the prediction errors. In particular, we propose to directly optimize basic loss functions of the prediction error metrics typically used in the literature, such as the mean absolute error or the worst case error. In some instances, a proper choice of the loss function can directly reduce reasonably the prediction performance in the desired metric, albeit at the cost of additional computations during training. To support this claim, we describe the statistical learning theoretic foundations, and provide supporting numerical evidence with the prediction of atomization energies for a database of small organic molecules.

2019, ,

We propose a new self-adaptive and double-loop smoothing algorithm to solve composite, nonsmooth, and constrained convex optimization problems. Our algorithm is based on Nesterov’s smoothing technique via general Bregman distance functions. It self-adaptively selects the number of iterations in the inner loop to achieve a desired complexity bound without requiring to set the accuracy a priori as in variants of augmented Lagrangian methods (ALM). We prove (1𝑘)-convergence rate on the last iterate of the outer sequence for both unconstrained and constrained settings in contrast to ergodic rates which are common in ALM as well as alternating direction method-of-multipliers literature. Compared to existing inexact ALM or quadratic penalty methods, our analysis does not rely on the worst-case bounds of the subproblem solved by the inner loop. Therefore, our algorithm can be viewed as a restarting technique applied to the ASGARD method in Tran-Dinh et al. (SIAM J Optim 28(1):96–134, 2018) but with rigorous theoretical guarantees or as an inexact ALM with explicit inner loop termination rules and adaptive parameters. Our algorithm only requires to initialize the parameters once, and automatically updates them during the iteration process without tuning. We illustrate the superiority of our methods via several examples as compared to the state-of-the-art.

2019,

We develop a new Newton Frank-Wolfe algorithm to solve a class of constrained self-concordant minimization problems using linear minimization oracles (LMO). Unlike L-smooth convex functions, where the Lipschitz continuity of the objective gradient holds globally, the class of self-concordant functions only has local bounds, making it difficult to estimate the number of linear minimization oracle (LMO) calls for the underlying optimization algorithm. Fortunately, we can still prove that the number of LMO calls of our method is nearly the same as that of the standard Frank-Wolfe method in the L-smooth case. Specifically, our method requires at most O(epsilon-(1+nu)) LMO's, where epsilon is the desired accuracy, and nu is an element of(0,0.139) is a given constant depending on the chosen initial point of the proposed algorithm. Our intensive numerical experiments on three applications: portfolio design with the competitive ratio, D-optimal experimental design, and logistic regression with elastic-net regularizer, show that the proposed Newton Frank-Wolfe method outperforms different state-of-the-art competitors.