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algassert.com | ||
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www.appletonaudio.com
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francisbach.com
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| | | | | [AI summary] The blog post discusses non-convex quadratic optimization problems and their solutions, including the use of strong duality, semidefinite programming (SDP) relaxations, and efficient algorithms. It highlights the importance of these problems in machine learning and optimization, particularly for non-convex problems where strong duality holds. The post also mentions the equivalence between certain non-convex problems and their convex relaxations, such as SDP, and provides examples of when these relaxations are tight or not. Key concepts include the role of eigenvalues in quadratic optimization, the use of Lagrange multipliers, and the application of methods like Newton-Raphson for solving these problems. The author also acknowledges contributions... | |
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nhigham.com
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| | | | | The spectral radius $latex \rho(A)$ of a square matrix $latex A\in\mathbb{C}^{n\times n}$ is the largest absolute value of any eigenvalue of $LATEX A$: $latex \notag \rho(A) = \max\{\, |\lambda|: \lambda~ \mbox{is an eigenvalue of}~ A\,\}. $ For Hermitian matrices (or more generally normal matrices, those satisfying $LATEX AA^* = A^*A$) the spectral radius is just... | |
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thenumb.at
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| | | [AI summary] This text provides an in-depth exploration of how functions can be treated as vectors, particularly in the context of signal and geometry processing. It discusses the representation of functions as infinite-dimensional vectors, the use of Fourier transforms in various domains (such as 1D, spherical, and mesh-based), and the application of linear algebra to functions for tasks like compression and smoothing. The text also touches on the mathematical foundations of these concepts, including the Laplace operator, eigenfunctions, and orthonormal bases. It concludes with a list of further reading topics and acknowledges the contributions of reviewers. | ||
