Question

Lagrangian of optimization problem with convex set constraint

score 32 · Answer 1 · 2026-04-09 03:47:30

32

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Lagrangian of optimization problem with convex set constraint

Published on 09 Apr 2026 - 3:47

score 215 · Answer 2 · 2026-04-14 03:34:42

215

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How to solve the parameters in Proximal Gradient Method?

Published on 14 Apr 2026 - 3:34

score 43 · Answer 3 · 2026-04-13 12:56:32

43

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If $J[f]$ is strictly convex in $f$, are the minima of $J[<h, h>]$ w.r.t. $h$ the same up to rotations?

Published on 13 Apr 2026 - 12:56

score 102 · Answer 4 · 2026-04-11 13:54:47

102

Views

Optimizing seemingly a simple exponential function

Published on 11 Apr 2026 - 13:54

score 56 · Answer 5 · 2026-04-13 19:30:18

56

Views

Let $x,y$ be nonzero vectors and $u$, $v$ be their unit vectors. Lower-bound $\|u-v\|$ as an increasing function of $\|x-y\|$

Published on 13 Apr 2026 - 19:30

score 318 · Answer 6 · 2026-04-11 07:55:05

318

Views

Optimizing sum of two exponential functions

Published on 11 Apr 2026 - 7:55

score 57 · Answer 7 · 2026-04-17 06:45:16

57

Views

Why is $X = \{x:3x_{1}^{2}-2x_{2}^{2}\geq1, x_{1},x_{2}\geq 0\}$ a convex set

Published on 17 Apr 2026 - 6:45

score 132 · Answer 8 · 2026-04-13 23:27:45

132

Views

For continuous optimizations, what is the condition that says an optimal value lies on the boundary and not in the interior?

Published on 13 Apr 2026 - 23:27

score 66 · Answer 9 · 2026-04-11 14:43:37

66

Views

Explain this solution (sensitivity in optimization problems)?

Published on 11 Apr 2026 - 14:43

score 1.4k · Answer 10 · 2026-04-13 08:52:00

1.4k

Views

How to prove the geometric mean is concave?

Published on 13 Apr 2026 - 8:52

score 187 · Answer 11 · 2026-04-14 11:40:07

187

Views

Uniqueness of convex combination under strict quasiconcavity

Published on 14 Apr 2026 - 11:40

score 207 · Answer 12 · 2026-04-15 06:44:34

207

Views

Curvature condition in inexact line search

Published on 15 Apr 2026 - 6:44

score 28 · Answer 13 · 2026-04-10 21:49:55

28

Views

Solution space exists, but with introduction of objective function 'linprog' returns no solution

Published on 10 Apr 2026 - 21:49

score 111 · Answer 14 · 2026-04-11 03:55:47

111

Views

A bounded differentiable convex function is constant

Published on 11 Apr 2026 - 3:55

score 185 · Answer 15 · 2026-04-16 05:21:54

185

Views

Extreme points of the convex set $S:=\{\mathbf x \in \mathbb R^n : \|L\mathbf x\|_{*}\le 1\}.$

Published on 16 Apr 2026 - 5:21

List Question

Lagrangian of optimization problem with convex set constraint

How to solve the parameters in Proximal Gradient Method?

If $J[f]$ is strictly convex in $f$, are the minima of $J[<h, h>]$ w.r.t. $h$ the same up to rotations?

Optimizing seemingly a simple exponential function

Let $x,y$ be nonzero vectors and $u$, $v$ be their unit vectors. Lower-bound $\|u-v\|$ as an increasing function of $\|x-y\|$

Optimizing sum of two exponential functions

Why is $X = \{x:3x_{1}^{2}-2x_{2}^{2}\geq1, x_{1},x_{2}\geq 0\}$ a convex set

For continuous optimizations, what is the condition that says an optimal value lies on the boundary and not in the interior?

Explain this solution (sensitivity in optimization problems)?

How to prove the geometric mean is concave?

Uniqueness of convex combination under strict quasiconcavity

Curvature condition in inexact line search

Solution space exists, but with introduction of objective function 'linprog' returns no solution

A bounded differentiable convex function is constant

Extreme points of the convex set $S:=\{\mathbf x \in \mathbb R^n : \|L\mathbf x\|_{*}\le 1\}.$

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