Hessian matrix
In mathematics, the Hessian matrix, Hessian or (less commonly) Hesse matrix is a square matrix of second-order partial derivatives of a scalar-valued function, or scalar field. It describes the local curvature of a function of many variables. The Hessian matrix was developed in the 19th century by the German mathematician Ludwig Otto Hesse and later named after him. Hesse originally used the term "functional determinants". The Hessian is sometimes denoted by H or, ambiguously, by ∇2.
Hessian matrix - Wikipedia Jump to content From Wikipedia, the free encyclopedia Matrix of second derivatives Part of a series of articles about Calculus ∫ a b f ′ ( t ) d t = f ( b ) − f ( a ) {\displaystyle \int _{a}^{b}f'(t)\,dt=f(b)-f(a)} Fundamental theorem Limits Continuity Rolle's theorem Mean value theorem Inverse function theorem Differential Definitions Derivative ( generalizations ) Differential infinitesimal of a function total Concepts Differentiation notation Second derivative Implicit differentiation Logarithmic differentiation Related rates Taylor's theorem Rules and identities
Explore this link on the map →related reading
- matrixcookbook.pdfmath.uwaterloo.ca
- Pen and Paper Exercises in Machine Learningarxiv.org
- The Matrix Calculus You Need For Deep Learningarxiv.org
- KFAC explainedfdangel.com
- https://www.deeplearningbook.org/contents/linear_algebra.htmldeeplearningbook.org
- An Intuitive Guide to Linear Algebra – BetterExplainedbetterexplained.com
- 2404.17625arxiv.org
- Proofs involving ordinary least squares - Wikipediaen.wikipedia.org
- Why Momentum Really Worksdistill.pub
- Napkin.pdfvenhance.github.io
- Gregory Gundersengregorygundersen.com
- Mathematical methods for economic theory: 3.3 Concave and convex functions of many variablesmjo.osborne.economics.utoronto.ca