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Convex Analysisの二,三の進展について
徹 丸山
- Vol. 70, Iss: 1, pp 97-119
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The article was published on 1977-02-01 and is currently open access. It has received 5933 citations till now.read more
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MPC for tracking zone regions
Antonio Ferramosca,Daniel Limon,Alejandro H. González,Alejandro H. González,Darci Odloak,Eduardo F. Camacho +5 more
TL;DR: In this article, a stable MPC formulation for constrained linear systems with several practical properties is developed for this scenario, and the concept of distance from a point to a set is exploited to propose an additional cost term, which ensures both recursive feasibility and local optimality.
Journal ArticleDOI
An alternating extragradient method for total variation-based image restoration from Poisson data
TL;DR: In this paper, an iterative method based on an alternating extragradient scheme was proposed to solve the primal-dual formulation of both total variation and hypersurface regularization problems.
Posted Content
Simultaneous support recovery in high dimensions: Benefits and perils of block $\ell_1/\ell_\infty$-regularization
S. Negahban,M. J. Wainwright +1 more
TL;DR: In this paper, the authors analyzed the high-dimensional scaling of a regularized version of quadratic programming with respect to consistency in the covariance matrix and variable selection, and established bounds on the error for exact variable selection for fixed and random designs.
Journal ArticleDOI
Relative Entropy Relaxations for Signomial Optimization
TL;DR: In this paper, a hierarchy of convex relaxations is proposed to obtain successively tighter lower bounds of the optimal value of SPs, which are obtained by solving increasingly larger-sized relative entropy optimization problems and are convex programs specified in terms of linear and relative entropy functions.
Journal ArticleDOI
Adaptive Poisson disorder problem
TL;DR: The objective is to design an alarm time which is adapted to the history of the arrival process and detects the disorder time as soon as possible, and assumes in this paper that the new arrival rate after the disorder is a random variable.
References
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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Increasing Returns and Long-Run Growth
TL;DR: In this paper, the authors present a fully specified model of long-run growth in which knowledge is assumed to be an input in production that has increasing marginal productivity, which is essentially a competitive equilibrium model with endogenous technological change.
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Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers
TL;DR: It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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An Algorithm for Vector Quantizer Design
Y. Linde,A. Buzo,Robert M. Gray +2 more
TL;DR: An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data.