Question

Showing that $\{\vec{x} : x_2 \geq e^{-x_1}\}$ is a convex set

score 288 · Answer 1 · 2018-09-21 02:09:24

288

Views

Showing that $\{\vec{x} : x_2 \geq e^{-x_1}\}$ is a convex set

Published on 21 Sep 2018 - 2:09

score 322 · Answer 2 · 2026-03-21 17:14:58

322

Views

How to derive this augmented Lagrangian function?

Published on 21 Mar 2026 - 17:14

score 835 · Answer 3 · 2018-09-21 19:02:04

835

Views

Convex Optimization - Derive Conjugate function

Published on 21 Sep 2018 - 19:02

score 577 · Answer 4 · 2026-03-25 06:24:27

577

Views

When are sublevel sets of convex subdifferentiable functions bounded?

Published on 25 Mar 2026 - 6:24

score 91 · Answer 5 · 2026-03-24 23:42:34

91

Views

How to convert a semidefinite program to an optimization problem that can be solved by the ellipsoid method?

Published on 24 Mar 2026 - 23:42

score 9.2k · Answer 6 · 2018-09-22 20:56:29

9.2k

Views

How would one prove that a linear combination of convex functions is also convex?

Published on 22 Sep 2018 - 20:56

score 908 · Answer 7 · 2026-03-27 15:16:48

908

Views

Clarification about the relation between maximization and minimization of objective functions

Published on 27 Mar 2026 - 15:16

score 297 · Answer 8 · 2026-03-26 17:33:31

297

Views

Prove or disprove that the set of probabilities $\{p \in \Delta^n \mid \mbox{Var}(X) \leq \alpha\}$ is a convex set.

Published on 26 Mar 2026 - 17:33

score 150 · Answer 9 · 2026-03-25 15:39:41

150

Views

Does this gradient descent with asymptotically vanishing stepsize converge?

Published on 25 Mar 2026 - 15:39

score 36 · Answer 10 · 2018-09-25 11:33:22

36

Views

Given the following two assumptions, How to conclude $\lim_{k \rightarrow \infty}|\nabla g_r(y^{k})|=0 \ $?

Published on 25 Sep 2018 - 11:33

score 150 · Answer 11 · 2026-03-26 02:51:19

150

Views

Converting a supermodular optimization problem to submodular optimization

Published on 26 Mar 2026 - 2:51

score 221 · Answer 12 · 2026-03-25 09:33:16

221

Views

Non-positive-definite quadratic program minimum simplification

Published on 25 Mar 2026 - 9:33

score 75 · Answer 13 · 2026-03-25 01:28:52

75

Views

Show that $\{p \in \Delta^n \mid b^T p + p^T Ap \leq \alpha\}$ is a convex set.

Published on 25 Mar 2026 - 1:28

score 296 · Answer 14 · 2026-03-25 13:59:50

296

Views

Finding minimum of $l-1$ norm of affine function under constrained using dual approach

Published on 25 Mar 2026 - 13:59

score 60 · Answer 15 · 2018-09-26 12:37:41

60

Views

Derivative of an Euclidean norm of vectors

Published on 26 Sep 2018 - 12:37

List Question

Showing that $\{\vec{x} : x_2 \geq e^{-x_1}\}$ is a convex set

How to derive this augmented Lagrangian function?

Convex Optimization - Derive Conjugate function

When are sublevel sets of convex subdifferentiable functions bounded?

How to convert a semidefinite program to an optimization problem that can be solved by the ellipsoid method?

How would one prove that a linear combination of convex functions is also convex?

Clarification about the relation between maximization and minimization of objective functions

Prove or disprove that the set of probabilities $\{p \in \Delta^n \mid \mbox{Var}(X) \leq \alpha\}$ is a convex set.

Does this gradient descent with asymptotically vanishing stepsize converge?

Given the following two assumptions, How to conclude $\lim_{k \rightarrow \infty}|\nabla g_r(y^{k})|=0 \ $?

Converting a supermodular optimization problem to submodular optimization

Non-positive-definite quadratic program minimum simplification

Show that $\{p \in \Delta^n \mid b^T p + p^T Ap \leq \alpha\}$ is a convex set.

Finding minimum of $l-1$ norm of affine function under constrained using dual approach

Derivative of an Euclidean norm of vectors

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