List questions from conjugate-gradient

Published on 10 Dec 2023 - 9:13

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Proof regarding the Conjugate Gradient Method

Published on 12 Dec 2023 - 20:20

41

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Prove a lemma of the conjugate gradient method

Published on 15 Dec 2023 - 16:25

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Show that to reduce the initial error by the factor $\varepsilon$ at most many iteration steps are required

Published on 11 Mar 2024 - 21:13

24

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Proximal operator of conjugate of Euclidean norm

Published on 22 Mar 2024 - 12:38

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Newton conjugate gradient algorithm

Published on 09 Jun 2016 - 8:17

138

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Do there exist Conjugate-Gradient algorithms for "nested" inverses.

Published on 01 Jun 2022 - 11:10

62

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Inequality in Conjugate gradient method for solving linear system

Published on 23 Jul 2022 - 14:29

158

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Descent direction with symmetrically positive definite hessian matrix and $∇f(x_{k})=0$ with $Hf(xk)d_{k}=−∇f(x_{k})$

Published on 27 Jul 2022 - 12:59

58

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Solving regression problem via conjugate gradient

Published on 17 Sep 2022 - 15:39

47

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Understanding how conjugate gradient is used in minimization?

Published on 04 Oct 2022 - 18:22

80

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Does CR for positive definite matrices have the same convergence theorem as CG?

Published on 26 Nov 2022 - 19:52

89

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Boyd & Vandenberghe, Exercise 3.36(e) — Conjugate of the negative geometric mean

Published on 06 Dec 2022 - 13:40

73

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Boyd & Vandenberghe, exercise 3.39 (c): Conjugate and minimization.

Published on 11 Dec 2022 - 11:41

33

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Unclear point: how do we know gradient at time step $t+1$ in conjugate gradient descent$\,$?

Published on 24 Dec 2022 - 19:58

List Question

How can one do fast bit-wise inverse derivative / gradient over $Z^2$ or $GF(2)$?

Proof regarding the Conjugate Gradient Method

Prove a lemma of the conjugate gradient method

Show that to reduce the initial error by the factor $\varepsilon$ at most many iteration steps are required

Proximal operator of conjugate of Euclidean norm

Newton conjugate gradient algorithm

Do there exist Conjugate-Gradient algorithms for "nested" inverses.

Inequality in Conjugate gradient method for solving linear system

Descent direction with symmetrically positive definite hessian matrix and $∇f(x_{k})=0$ with $Hf(xk)d_{k}=−∇f(x_{k})$

Solving regression problem via conjugate gradient

Understanding how conjugate gradient is used in minimization?

Does CR for positive definite matrices have the same convergence theorem as CG?

Boyd & Vandenberghe, Exercise 3.36(e) — Conjugate of the negative geometric mean

Boyd & Vandenberghe, exercise 3.39 (c): Conjugate and minimization.

Unclear point: how do we know gradient at time step $t+1$ in conjugate gradient descent$\,$?

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