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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.

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Constraint Databases

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Sparse Optimization with Least-Squares Constraints

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Computational and statistical tradeoffs via convex relaxation

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Suspense and surprise

TL;DR: In this article, the authors model demand for noninstrumental information, drawing on the idea that people derive entertainment utility from suspense and surprise, and analyze the optimal way to reveal information over time so as to maximize expected suspense or surprise experienced by a Bayesian audience.
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Deep Learning

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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

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.