List questions from convex-optimization

Published on 05 Apr 2018 - 9:17

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Normalized steepest descent

Published on 25 Mar 2026 - 5:59

101

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Is this expression concave or convex?

Published on 05 Apr 2018 - 14:01

192

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Algorithms for the minimization of pseudoconvex functions

Published on 05 Apr 2018 - 15:32

177

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The dual cone of $\{(x, t) \,|\, ||x||_1 \le t\}$

Published on 05 Apr 2018 - 16:57

89

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SOCP Complementary Slackness Condition Confusion

Published on 23 Mar 2026 - 1:03

75

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Best Linear Over-Estimator of Shifted Step-Function

Published on 07 Apr 2018 - 1:25

13

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Recovering a convex subset from its projections onto rays

Published on 07 Apr 2018 - 10:14

113

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Convex Cones are closed under addition, can we infer from $A + B \in C$ that $A, B \in C$?

Published on 25 Mar 2026 - 3:03

209

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Is the directional derivative $f'(x;d)$ ALWAYS equal to $\nabla f^T d$? or only under certain conditions?

Published on 25 Mar 2026 - 6:06

49

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What are the requirements on the function f(x) for the L_2 norm of f(x) to be a convex function?

Published on 23 Mar 2026 - 17:47

99

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Prove that if $\nabla f(x_0) = 0$ and $\nabla^2f(x_0) \succeq0$ then $x_0$ couldn't be local minimum.

Published on 08 Apr 2018 - 1:49

267

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Convexity in constrained optimization

Published on 25 Mar 2026 - 14:19

61

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Positive semidefiniteness of symmetric matrix

Published on 08 Apr 2018 - 18:02

225

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Close Form Solution of Quasi Concave Maximization Problem - $ \arg \max_{x \in \mathbb{R}_{++}} \frac{ \ln(1+ax) + \ln(1+bx) }{x+c} $

Published on 09 Apr 2018 - 8:38

List Question

How to prove the dual of a norm

Normalized steepest descent

Is this expression concave or convex?

Algorithms for the minimization of pseudoconvex functions

The dual cone of $\{(x, t) \,|\, ||x||_1 \le t\}$

SOCP Complementary Slackness Condition Confusion

Best Linear Over-Estimator of Shifted Step-Function

Recovering a convex subset from its projections onto rays

Convex Cones are closed under addition, can we infer from $A + B \in C$ that $A, B \in C$?

Is the directional derivative $f'(x;d)$ ALWAYS equal to $\nabla f^T d$? or only under certain conditions?

What are the requirements on the function f(x) for the L_2 norm of f(x) to be a convex function?

Prove that if $\nabla f(x_0) = 0$ and $\nabla^2f(x_0) \succeq0$ then $x_0$ couldn't be local minimum.

Convexity in constrained optimization

Positive semidefiniteness of symmetric matrix

Close Form Solution of Quasi Concave Maximization Problem - $ \arg \max_{x \in \mathbb{R}_{++}} \frac{ \ln(1+ax) + \ln(1+bx) }{x+c} $

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