Concept

Convex analysis

Related publications (151)

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In this paper, a data-driven method for controller design with constraints on the positive-realness of closed-loop transfer functions over an arbitrary set of frequencies is proposed. The positive-realness of a closed-loop transfer function is represented ...

IEEE2020

Linearly Convergent Frank-Wolfe with Backtracking Line-Search

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Structured constraints in Machine Learning have recently brought the Frank-Wolfe (FW) family of algorithms back into the spotlight. While the classical FW algorithm has poor local convergence properties, Away-steps FW and Pairwise FW have emerged as improv ...

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Random extrapolation for primal-dual coordinate descent

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We introduce a randomly extrapolated primal-dual coordinate descent method that adapts to sparsity of the data matrix and the favorable structures of the objective function. Our method updates only a subset of primal and dual variables with sparse data, an ...

2020

Discrete-time regional pole-placement using convex approximations: Theory and application to a boost converter

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The problem of allocating the closed-loop poles of linear systems in specific regions of the complex plane defined by discrete time-domain requirements is addressed. The resulting non-convex set is inner-approximated by a convex region described with linea ...

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Robust control of systems with sector nonlinearities via convex optimization: A data-driven approach

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In this paper, a new data-driven method for designing robust controllers is proposed for systemswith sector-bounded nonlinearities and multimodel uncertainties. The results from the circle criterion are used to generate necessary and sufficient convex cons ...

WILEY2019

On the convergence of stochastic primal-dual hybrid gradient

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Tensor Robust Pca On Graphs

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We propose a graph signal processing framework to overcome the computational burden of Tensor Robust PCA (TRPCA). Our framework also serves as a convex alternative to graph regularized tensor factorization methods. Our method is based on projecting a tenso ...

IEEE2019

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We propose a stochastic gradient framework for solving stochastic composite convex optimization problems with (possibly) infinite number of linear inclusion constraints that need to be satisfied almost surely. We use smoothing and homotopy techniques to ha ...

2019

Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees

Nisheeth Vishnoi, Laura Elisa Celis, Vijay Keswani, Lingxiao Huang

Developing classification algorithms that are fair with respect to sensitive attributes of the data is an important problem due to the increased deployment of classification algorithms in societal contexts. Several recent works have focused on studying cla ...

ASSOC COMPUTING MACHINERY2019

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