Question

$f$ convex $\iff$ $f(y)\ge f(x)+\nabla f(x)(x-y)$

score 214 · Answer 1 · 2026-04-17 13:50:38

214

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$f$ convex $\iff$ $f(y)\ge f(x)+\nabla f(x)(x-y)$

Published on 17 Apr 2026 - 13:50

score 47 · Answer 2 · 2026-04-16 21:45:39

47

Views

Lagrange multipliers and derivative of Lagrangian

Published on 16 Apr 2026 - 21:45

score 29 · Answer 3 · 2026-04-14 16:55:32

29

Views

$f\in C^1$ convex, if exists $x^\in S$ such that $\nabla^t f(x^)(y-x^)\ge 0$ then $x^$ is a global minimizer of $f$

Published on 14 Apr 2026 - 16:55

score 1.2k · Answer 4 · 2026-04-16 14:43:19

1.2k

Views

Proving $\max\{f,g\}$, $\sum x_i^2$ and $e^{f(x)}$ are convex functions

Published on 16 Apr 2026 - 14:43

score 65 · Answer 5 · 2026-04-13 02:35:38

65

Views

From Ng video: Using feature normalization with polynomial regression

Published on 13 Apr 2026 - 2:35

score 155 · Answer 6 · 2026-04-16 00:54:19

155

Views

Gradient descent for functionals?

Published on 16 Apr 2026 - 0:54

score 217 · Answer 7 · 2026-04-15 19:26:24

217

Views

Maximizing $2\tan^{-1} x_1 + x_2$

Published on 15 Apr 2026 - 19:26

score 88 · Answer 8 · 2026-03-28 03:53:37

88

Views

Is there any sufficient optimality conditions without constraints qualifications?

Published on 28 Mar 2026 - 3:53

score 229 · Answer 9 · 2026-03-25 16:01:53

229

Views

Optimal strategy for the biggest interval conquest?( Game similar to Nim )

Published on 25 Mar 2026 - 16:01

score 57 · Answer 10 · 2026-04-15 16:41:01

57

Views

Proving $\nabla^2 f$ continuous, $\nabla f(x^)=0$ and $\nabla^2 f(x^)>0$ then $x^*$ is strict local minimizer

Published on 15 Apr 2026 - 16:41

score 115 · Answer 11 · 2026-04-15 07:01:20

115

Views

Modification of Standard LASSO Problem for $N$ Observations

Published on 15 Apr 2026 - 7:01

score 178 · Answer 12 · 2026-04-15 05:48:32

178

Views

How $||\nabla f(x) - \nabla f(\overline{x})||\le L|x-\overline{x}|$ implies $(\nabla f(x) -\nabla f(\overline{x}))^Tp_k\le L|x-\overline{x}||p_k|$

Published on 15 Apr 2026 - 5:48

score 548 · Answer 13 · 2026-03-30 12:22:50

548

Views

gradient descent method with linear exact search we have $\nabla^t f(x^k)\nabla f(x^{k+1})=0$

Published on 30 Mar 2026 - 12:22

score 208 · Answer 14 · 2026-04-14 19:20:14

208

Views

Find ratio between volume of optimal cuboid bound in ellipse, and volume of the ellipse.

Published on 14 Apr 2026 - 19:20

score 246 · Answer 15 · 2026-04-15 20:34:01

246

Views

What is the general expression for the gradient of $\dfrac{1}{2} x^TA(x)x$?

Published on 15 Apr 2026 - 20:34

List Question

$f$ convex $\iff$ $f(y)\ge f(x)+\nabla f(x)(x-y)$

Lagrange multipliers and derivative of Lagrangian

$f\in C^1$ convex, if exists $x^\in S$ such that $\nabla^t f(x^)(y-x^)\ge 0$ then $x^$ is a global minimizer of $f$

Proving $\max\{f,g\}$, $\sum x_i^2$ and $e^{f(x)}$ are convex functions

From Ng video: Using feature normalization with polynomial regression

Gradient descent for functionals?

Maximizing $2\tan^{-1} x_1 + x_2$

Is there any sufficient optimality conditions without constraints qualifications?

Optimal strategy for the biggest interval conquest?( Game similar to Nim )

Proving $\nabla^2 f$ continuous, $\nabla f(x^)=0$ and $\nabla^2 f(x^)>0$ then $x^*$ is strict local minimizer

Modification of Standard LASSO Problem for $N$ Observations

How $||\nabla f(x) - \nabla f(\overline{x})||\le L|x-\overline{x}|$ implies $(\nabla f(x) -\nabla f(\overline{x}))^Tp_k\le L|x-\overline{x}||p_k|$

gradient descent method with linear exact search we have $\nabla^t f(x^k)\nabla f(x^{k+1})=0$

Find ratio between volume of optimal cuboid bound in ellipse, and volume of the ellipse.

What is the general expression for the gradient of $\dfrac{1}{2} x^TA(x)x$?

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