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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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Liquidation in Limit Order Books with Controlled Intensity
TL;DR: A framework for solving optimal liquidation problems in limit order books is considered and it is shown that the discrete state problem and its optimal solution converge to the corresponding quantities in the continuous selling limit uniformly on compacts.
Journal ArticleDOI
A Variational Approach to Video Registration with Subspace Constraints.
TL;DR: This paper exploits the high correlation between 2D trajectories of different points on the same non-rigid surface by assuming that the displacement of any point throughout the sequence can be expressed in a compact way as a linear combination of a low-rank motion basis.
Journal ArticleDOI
Optimal investment with random endowments in incomplete markets
Julien Hugonnier,Dmitry Kramkov +1 more
TL;DR: In this article, the problem of expected utility maximization of an agent who, in addition to an initial capital, receives random endowments at maturity is studied, and it is shown that this approach leads to a dual problem, whose solution is always attained in the space of random variables.
Posted Content
Information-theoretic lower bounds on the oracle complexity of convex optimization
TL;DR: The complexity of stochastic convex optimization in an oracle model of computation is studied and tight minimax complexity estimates are obtained for various function classes.
Journal ArticleDOI
Brief paper: Segmentation of ARX-models using sum-of-norms regularization
TL;DR: Segmentation of time-varying systems and signals into models whose parameters are piecewise constant in time is formulated as a least-squares problem with sum-of-norms regularization over the state parameter jumps, a generalization of @?"1-regularization.
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.
Book
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.