# Tagged Questions

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### Easy elementary proof of Farkas Lemma?

Is there any elementary proof of Farkas lemma which does not use convex analysis and hyperplane separation theorem? What about special case below: If the Matrix $A$ is invertible, then there is ...
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### Shrinkage operator for matrices

Here http://web.stanford.edu/~boyd/papers/pdf/prox_algs.pdf, on page 188, you can see the derivation of the soft thresholding operator or shrinkage operator for the case of vectors using Moreau ...
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### Applications of low-rank matrix approximation

There was a similar question here Use of low rank approximation of a matrix that has unfortunately remained unanswered. Although being along the same lines, my question will be formulated in a little ...
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### Solving the cost function optimization problem using linear programming

My cost function is in the form $$\Delta u^T P \Delta u + q^T \Delta u$$How shall I put it in the form of $c^T x$ to be able to solve it using linear programming?
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### How to calc $\min ||J\Delta\tau + D||_*$

How to calculate $$\min_{\tau} ||J_1 \tau_1 + \cdots + J_p \tau_p + D ||_*$$ where $\tau_1, \cdots, \tau_p \in \mathbb{R}$ $J_1, \cdots, J_p, D \in \mathbb{R}^{m \times n}$ $||\cdot||_*$ is sum ...
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### How to show this algorithm on positive semidefinite matrices converges to a global maximum determinant

I'm dealing with an algorithm which is supposed to converge to the maximum determinant of certain positive semidefinite matrices. The problem is that we have such a matrix, and we vary certain ...
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### How to find closest positive definite matrix of non-symmetric matrix

I have a matrix A given and I want to find the matrix B which is closest to A in the frobenius norm and is positiv definite. B does not need to be symmetric. I found a lot of solutions if the input ...
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### Minimum L1 norm may not obtain the sparsest solution?

Here is a paper called For Most Large Underdetermined Systems of Equations, the Minimal L1-norm Near-Solution Approximates the Sparsest Near-Solution. However, I did not quite get its definition of ...
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### How to prove a set is convex

Let $E = \{x\mid (x - c)^{T} P^{-1} (x-c) \le 1 \}$, where $P$ is symmetric positive definite. Show that $E$ is convex. Here is what I did. It seems like $E$ is an ellipsoid. We want to show that ...
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### Optimization objective modification for matrix factorization.

I have the following optimization objective: Here A is an mXn user-item rating matrix. W is a nXn weight vector that I am trying to learn. beta and lambda are parameters passed in. ...
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### Why does the non-negative matrix factorization problem non-convex?

Supposing $\mathbf{X}\in\mathbb{R}_+^{m\times n}$, $\mathbf{Y}\in\mathbb{R}_+^{m\times r}$, $\mathbf{W}\in\mathbb{R}_+^{r\times n}$, the non-negative matrix factorization problem is defined as: ...
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### Positive semidefinite Matrix examples query

This might be really dumb question but I've just started dealing with such matrices. I would like to know why $$\begin{bmatrix} 0 & 1 \\ 1 & x \end{bmatrix}$$ cannot be a positive semidefinite ...
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### Rewriting a quadratic Matrix equation as a quadratic vector equation

Consider the set of $N \times N$ matrices $\{W_i\}_{i=1}^{i=L}$, set of $N \times 1$vectors $\{g_i\}_{i=1}^{i=L}$ and $\{h_i\}_{i=1}^{i=L}$. Now consider the following sum \begin{align} ...