# Questions tagged [disciplined-quasiconvex-programming]

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### How is this problem quasi-convex?

I'm currently reading the following paper B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. I. Jordan, and S. S. Sastry, “Kalman filtering with intermittent observations,” IEEE Transactions ...
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### How to write this objective in CVXPY for quasiconvex programming?

I have the following objective that I want to maximize: \max_{U_T\in \mathbb{R}, x\in\mathbb{R}^T} J_\alpha(U_T) = \frac{\alpha}{\alpha-1}\log\left(\frac{\cosh(U_T)}{\cosh(\alpha U_T)^...
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1 vote
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### Handling Variable Division in CVXPY for Calculating Annualized Rate of Change

I am working with a dataset that contains multiple entries for different IDs across various years. Some IDs might have a gap of years between entries. My goal is to solve an optimization problem using ...
• 111
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### On a clarification on usage of inequalities in convex programming

The inequality $x^3\leq y$ is not convex. But $0<x$ added to the above provides a convex region. My question is whether in convex programming it is allowed to use both inequalities together and use ...
• 131
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### How to minimize a quasi-convex function in 2 dimensions?

I know that if $f$ is a quasi-convex function in one dimension (that is, $f: \mathbb{R} \to \mathbb{R}$), then we can use the 'golden section' line search to find the optimizer. Now suppose I have a ...
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### Piecewise linear and global optimization

I am new to OR, and apologies if my mathematical notation is not clear. I have tried my best to keep it concise and given an explanation with numerical data. I would like to understand: Can this ...
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152 views

### How to linearize this multiplicative constraint?

I have a constraint in the form $\sqrt{|\sum_{c\in C}{h_cw_c}|^2}\ge\sqrt{x}\zeta$ Here, $h_c$ is s row vector (know), $w_c$ is a column vector (variable). $x$ and $\zeta$ are also optimization ...
• 2,211
I'm using cvxpy to model a problem. Inside a very large and complex LP, I create two continuous, affine (unconstrained) expressions: $x$ and $y$. Due to how they ...