query: Minimize function of L2 and L1 vector's norm
positive_passages.docid: 21014
positive_passages.text: $ {L}_{1} $ Regularized Unconstrained Optimization Problem
negative_passages.docid: 122300
negative_passages.text: Maximizing a quadratic function subject to $\| x \|_2 \le 1$
negative_passages.docid: 138457
negative_passages.text: Seminorms: Absolute Convexity
negative_passages.docid: 7740
negative_passages.text: The median minimizes the sum of absolute deviations (the $ {\ell}_{1} $ norm)
negative_passages.docid: 158704
negative_passages.text: How to minimize $\sum_{i=1}^p (y_i-x_i)^2$ with constraints $\sum_{i=1}^p y_i - 1 =0$ and $\forall i=\overline{1,p}:-y_i \le 0$?
negative_passages.docid: 146405
negative_passages.text: Norm with symmetric positive definite matrix
negative_passages.docid: 104390
negative_passages.text: How can we rewrite this optimization problem in standard form?
negative_passages.docid: 113594
negative_passages.text: Convex combination of quasiconvex functions.
negative_passages.docid: 91056
negative_passages.text: How can I implement the method of Lagrange multipliers to find constrained extrema?
negative_passages.docid: 127263
negative_passages.text: Showing the Equivalence Between the $ {L}_{2} $ Norm Regularized Regression and $ {L}_{2} $ Norm Constrained Regression Using KKT
negative_passages.docid: 104378
negative_passages.text: Minimizing weighted sum
negative_passages.docid: 36221
negative_passages.text: L1 & L2 Regularization in Light GBM
negative_passages.docid: 17893
negative_passages.text: How do I show that the mean of the posterior density minimizes this squared error loss function?
negative_passages.docid: 25296
negative_passages.text: Difference between least squares and minimum norm solution
negative_passages.docid: 62425
negative_passages.text: Equivalent Definitions of the Operator Norm
negative_passages.docid: 36099
negative_passages.text: Show that $\|f\|^{2}$ attains a minimum value on the interior of $B$
negative_passages.docid: 127634
negative_passages.text: query on maximum minimum of multi-variable function