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mcyoung.xyz | ||
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blog.mecheye.net
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| | | | | [AI summary] The article provides an in-depth guide to matrix multiplication and ordering in computer graphics, focusing on column-vector and row-vector conventions, column-major and row-major packing, and the implications of these choices. It emphasizes the importance of consistency and clarity in matrix conventions, as well as the use of transposes to reconcile different representations. The key takeaways include the equivalence of A * B and transpose(B) * transpose(A), the ability to combine multiple transformations into a single matrix, and the impact of matrix packing on memory efficiency. | |
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andrea.corbellini.name
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| | | | | [AI summary] The text provides an in-depth explanation of elliptic curve cryptography (ECC), covering fundamental concepts such as elliptic curves over finite fields, point addition, cyclic subgroups, subgroup orders, and the discrete logarithm problem. It also discusses practical aspects like finding base points, cofactors, and the importance of choosing subgroups with high order for cryptographic security. The text emphasizes that ECC relies on the difficulty of solving the discrete logarithm problem on elliptic curves, which is considered computationally hard and forms the basis for secure cryptographic protocols like ECDH and ECDSA. | |
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susam.net
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| | | | | [AI summary] This article explains why the product of two negative numbers is positive, using mathematical proofs and ring axioms to generalize the rule beyond basic arithmetic. | |
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nhigham.com
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| | | In linear algebra terms, a correlation matrix is a symmetric positive semidefinite matrix with unit diagonal. In other words, it is a symmetric matrix with ones on the diagonal whose eigenvalues are all nonnegative. The term comes from statistics. If $latex x_1, x_2, \dots, x_n$ are column vectors with $latex m$ elements, each vector containing... | ||
