Question

Show that the operator $Lu$ is linear, symmetric, positive and coercive operator.

score 154 · Answer 1 · 2026-04-06 13:08:58

154

Views

Show that the operator $Lu$ is linear, symmetric, positive and coercive operator.

Published on 06 Apr 2026 - 13:08

score 136 · Answer 2 · 2026-03-25 11:16:07

136

Views

I cannot find the general formula for least perimeter when a specific number of squares are given

Published on 25 Mar 2026 - 11:16

score 338 · Answer 3 · 2026-03-26 12:06:37

338

Views

Maximize the spectral norm of a correlation matrix by scrambling its off-diagonal elements

Published on 26 Mar 2026 - 12:06

score 42 · Answer 4 · 2018-03-15 12:08:35

42

Views

The mapping of a cost vector to the set of solutions of the respective LP is concave

Published on 15 Mar 2018 - 12:08

score 9.9k · Answer 5 · 2026-03-29 18:34:55

9.9k

Views

How Does $ {L}_{1} $ Regularization Present Itself in Gradient Descent?

Published on 29 Mar 2026 - 18:34

score 110 · Answer 6 · 2026-03-24 20:36:57

110

Views

Ambiguity about the Lagrangian method of optimization

Published on 24 Mar 2026 - 20:36

score 1k · Answer 7 · 2026-03-25 19:06:47

1k

Views

Is the expectation of the minimum of a function equal to the minimum of the expectation?

Published on 25 Mar 2026 - 19:06

score 155 · Answer 8 · 2026-04-04 02:52:35

155

Views

How can I evenly distribute skill among teams.

Published on 04 Apr 2026 - 2:52

score 168 · Answer 9 · 2026-04-02 02:52:03

168

Views

Is the number of boundary conditions sufficient for a unique solution?

Published on 02 Apr 2026 - 2:52

score 24 · Answer 10 · 2018-03-15 20:15:13

24

Views

Optimizing the arrangement of boxes

Published on 15 Mar 2018 - 20:15

score 39 · Answer 11 · 2026-04-06 17:11:46

39

Views

If the Hessian is simply a local quadratic, why can't we approximate it with a spline model?

Published on 06 Apr 2026 - 17:11

score 3.1k · Answer 12 · 2026-03-25 06:02:31

3.1k

Views

Why don't people do simulated annealing before gradient descent?

Published on 25 Mar 2026 - 6:02

score 23 · Answer 13 · 2026-03-25 16:00:09

23

Views

To show transformed problem as convex or not

Published on 25 Mar 2026 - 16:00

score 42 · Answer 14 · 2026-03-25 14:26:50

42

Views

Proving that the optimal solution of an LP is a point on the convex hull

Published on 25 Mar 2026 - 14:26

score 62 · Answer 15 · 2026-03-25 14:19:40

62

Views

Optimization problem on infinite dimensional space

Published on 25 Mar 2026 - 14:19

List Question

Show that the operator $Lu$ is linear, symmetric, positive and coercive operator.

I cannot find the general formula for least perimeter when a specific number of squares are given

Maximize the spectral norm of a correlation matrix by scrambling its off-diagonal elements

The mapping of a cost vector to the set of solutions of the respective LP is concave

How Does $ {L}_{1} $ Regularization Present Itself in Gradient Descent?

Ambiguity about the Lagrangian method of optimization

Is the expectation of the minimum of a function equal to the minimum of the expectation?

How can I evenly distribute skill among teams.

Is the number of boundary conditions sufficient for a unique solution?

Optimizing the arrangement of boxes

If the Hessian is simply a local quadratic, why can't we approximate it with a spline model?

Why don't people do simulated annealing before gradient descent?

To show transformed problem as convex or not

Proving that the optimal solution of an LP is a point on the convex hull

Optimization problem on infinite dimensional space

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