Question

How do I obtain the primal and dual for the regression estimator $\min _\beta[\|\beta\|^2+\sum_{i=1}^n \xi_i^2]$ s.t. $\xi_i=y_i-h(x_i)^\top \beta$?

score 30 · Answer 1 · 2026-04-11 22:59:01

30

Views

How do I obtain the primal and dual for the regression estimator $\min _\beta[\|\beta\|^2+\sum_{i=1}^n \xi_i^2]$ s.t. $\xi_i=y_i-h(x_i)^\top \beta$?

Published on 11 Apr 2026 - 22:59

score 91 · Answer 2 · 2026-04-12 18:35:03

91

Views

Does subtracting a rank-1 matrix from a positive definite matrix result in positive semi-definite matrix?

Published on 12 Apr 2026 - 18:35

score 41 · Answer 3 · 2026-04-09 05:23:56

41

Views

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Published on 09 Apr 2026 - 5:23

score 59 · Answer 4 · 2026-04-12 17:00:57

59

Views

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Published on 12 Apr 2026 - 17:00

score 293 · Answer 5 · 2023-09-06 16:02:50

293

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score 112 · Answer 6 · 2023-09-09 14:33:42

112

Views

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score 98 · Answer 7 · 2026-04-09 10:08:23

98

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score 31 · Answer 8 · 2026-04-12 02:03:30

31

Views

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score 109 · Answer 9 · 2026-04-11 11:45:18

109

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score 83 · Answer 10 · 2026-04-09 19:11:07

83

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score 117 · Answer 11 · 2026-04-11 10:30:30

117

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score 22 · Answer 12 · 2026-04-09 13:37:01

22

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score 293 · Answer 13 · 2026-04-11 02:42:40

293

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score 58 · Answer 14 · 2026-04-09 12:00:30

58

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Published on 09 Apr 2026 - 12:00

score 35 · Answer 15 · 2026-04-07 01:45:00

35

Views

Minimizing sum of square of three distances under monotone transformation -- is the solution unique?

Published on 07 Apr 2026 - 1:45

List Question

How do I obtain the primal and dual for the regression estimator $\min _\beta[\|\beta\|^2+\sum_{i=1}^n \xi_i^2]$ s.t. $\xi_i=y_i-h(x_i)^\top \beta$?

Does subtracting a rank-1 matrix from a positive definite matrix result in positive semi-definite matrix?

Could a linear programming problem be solved by dual (subgradient) methods?

Is the norm of projections onto convex sets a convave function?

How to prove $q=f(x)-\frac{m}{2}||x||^2$ is $(L-m)$-Lipschitz if $f$ is $m$-strongly convex with $L$-Lipschitz?

Squared $L_2$ norm is strongly convex w.r.t. $L_1$ norm

Is a posynomial concave under the following conditions?

Rewriting a maximization problem to a minimization one

How to see Lasso penalty is convex?

When the Karush-Kuhn-Tucker conditions fail to apply?

Directional derivative of proximal mapping of a convex function

how to solve for wasserstein duality easily in a special case when 2-Wasserstein inequality constraint is binding

Intuition behind the Euclidean orthogonal projection

Is it possible to make a posynomial concave using a change of variables?

Minimizing sum of square of three distances under monotone transformation -- is the solution unique?

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