# Questions tagged [cvx]

a MATLAB-based modeling framework for convex optimization.

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### Minimizing $\mathrm{trace}(S)+\mathrm{trace}(S^{-2})$ using CVX

In Matlab, I would like to minimize the function $$f(S)=\mathrm{trace}(S)+\mathrm{trace}(S^{-2})$$ where $S \in \mathcal{M}_{m,m}$ is symmetric and positive definite, which is definitely a convex ...
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### Issues with CVX package for optimization

I am trying to use the cvx package for optimization. However, I am having some issues with it. I have a variable X which is a matrix but I cannot add $X^{-1}$ in the objective function. What should I ...
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### Sign or cardinality constraint when solving for sparse signal

I'm currently learning about using linear and semidefinite programming to find sparse solutions to problems. In particular, finding sparse solutions where the sampling functions are sinusoidal (...
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### How do I correctly multiply vectors and matrices in Python and MATLAB?

I have been trying for 2-3 days now to get L2 regularized logistric regression to work in Matlab (CVX) and Python(CVXPY) but no success. I am fairly new to convex optimization so I am quite frustrated....
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### Solving rank deficient systems with cvx

I am using cvx to solve linear programs with constraints of the form $Ax=b,x\ge0$. However the matrix $A$ is rank deficient and cvx returns a warning and finally displays status as 'Infeasible'. Rank ...
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### Formulate and solve a simple conic programs in cvxpy language [closed]

Let $r,\epsilon > 0$ and $a, b \in \mathbb R^n$ with $\|a\|_2 \le r$. Define $C(a) := \{x \in \mathbb R^p | \|x+a\|_2 \le r,\;\|x\|_\infty \le \epsilon\}$, and assume it is non-empty. Question (A)...
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### Positive definite matrix in CVX

I'm trying to use CVX to solve SDP problem. I have a constraint with positive definite matrix, but if i read the document of CVX, I can only find variable with positive semidefinite matrix. Can anyone ...
In CVX, how do we return the value of the parameter over which the problem is minimized at the optimal value? By this, I mean, how do we obtain $$x^* = \arg\min_x f(x)$$ when solving the problem ...