List questions from numerical-optimization

Published on 28 Mar 2026 - 11:15

32

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Estimate a vector given pairwise differences

Published on 16 Oct 2023 - 4:30

56

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Suppose that $f − \frac{1}{2}m||x||^2$ is convex where $f$ is differentiable.

Published on 17 Oct 2023 - 5:38

40

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Proof for strongly convex functions

Published on 17 Oct 2023 - 6:15

69

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Show that if $ 0 < c2 < c1 < 1$, there may be no step lengths that satisfy the Wolfe conditions.

Published on 17 Oct 2023 - 7:21

15

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Numerical optimisation of time series / balance sheet

Published on 05 Mar 2026 - 6:05

32

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Does this optimization algorithm have a name?

Published on 21 Oct 2023 - 2:16

27

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Equalise point-distance when optimising points on graph

Published on 27 Mar 2026 - 0:00

20

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Show the convergence of a numerical algorithm (gradient type)

Published on 24 Oct 2023 - 21:25

65

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Conjugate gradient descent when $A$ is non-singular but not symmetric

Published on 28 Oct 2023 - 0:56

44

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Convergence of numerical algorithm based on projection theorem

Published on 28 Oct 2023 - 10:09

18

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Show that $DLD^{-1} = dL$ for $D$ a diagonal matrix, $L$ a sub-diagonal matrix, and $U$ a super-diagonal matrix

Published on 29 Oct 2023 - 22:07

60

Views

How to numerically optimize a function with 'discontinuous' box constraints?

Published on 25 Mar 2026 - 9:53

27

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Looking for a source to cite for the fact that conjugate gradient converges more slowly when matrix is positive semidefinite

Published on 24 Feb 2026 - 13:38

68

Views

Instability with the Sherman Morrison Woodbury Formula?

Published on 14 Nov 2023 - 23:22

List Question

Black-box optimization of a symmetric function

Estimate a vector given pairwise differences

Suppose that $f − \frac{1}{2}m||x||^2$ is convex where $f$ is differentiable.

Proof for strongly convex functions

Show that if $ 0 < c2 < c1 < 1$, there may be no step lengths that satisfy the Wolfe conditions.

Numerical optimisation of time series / balance sheet

Does this optimization algorithm have a name?

Equalise point-distance when optimising points on graph

Show the convergence of a numerical algorithm (gradient type)

Conjugate gradient descent when $A$ is non-singular but not symmetric

Convergence of numerical algorithm based on projection theorem

Show that $DLD^{-1} = dL$ for $D$ a diagonal matrix, $L$ a sub-diagonal matrix, and $U$ a super-diagonal matrix

How to numerically optimize a function with 'discontinuous' box constraints?

Looking for a source to cite for the fact that conjugate gradient converges more slowly when matrix is positive semidefinite

Instability with the Sherman Morrison Woodbury Formula?

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