Subgradient

Protected: Fundamentals of convex analysis in stochastic optimization (1) Convex functions and subdifferentials, dual functions

Convex functions and subdifferentials, dual functions (convex functions, conjugate functions, Young-Fenchel inequality, subdifferentials, Lejandre transform, subgradient, L1 norm, relative interior points, affine envelope, affine set, closed envelope, epigraph, convex envelope, smooth convex functions, narrowly convex functions, truly convex closed functions, closed convex closed functions, execution domain, convex set) in basic matters of convex analysis in stochastic optimization used for Digital Transformation, Artificial Intelligence, Machine Learning tasks.

2023.04.18

アルゴリズム:Algorithmsグラフ理論スパースモデリング幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Fundamentals of Convex Analysis as a Basic Matter for Sequential Optimization in Machine Learning

Basics of convex analysis as a fundamental matter of continuous optimization utilized in digital transformation, artificial intelligence, and machine learning tasks subgradient, subdifferential, conjugate function, closed truly convex function, conjugate function, strongly convex function, closed truly convex function, upper and lower bounds on function values, Hesse matrix, epigraph, Taylor's theorem, relative interior, Ahuynh envelope, continuity, convex envelope, convex function, convex set

2022.11.10

微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory