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# Questions tagged [convex-optimization]

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### Finding solution of LP with maximum number of integral values [closed]

Is it possible to find an optimal solution to an LP with as many variables in the solution having integral value? Or less generally, if the LP has an integral optimal solution, can I find it? Many ...
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### Is the Chi-square divergence a Bregman divergence?

Is the Chi-squared divergence $\sum_{i} \frac{(x(i)-y(i))^2}{x(i)}$ a Bregman divergence? I.e., can it be written as $\phi(x) - \phi(y) - \langle\phi'(y),x-y\rangle$? If so, what is the potential ...
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### When can convex optimization be considered to be exactly solvable?

If one is trying to find the global minima of a convex function using gradient descent then one will get a run-time which is a function of $\epsilon >0$ where $\epsilon$ measures the accuracy of ...
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### On complexity of linear programming with quadratic equality/inequality constraints?

Feasibility test in Linear programming is in $P$ and in convex quadratic programming is in $P$. What is the maximum $k$ such that $n$-variable $m=poly(n)$ linear constraint feasibility test with $k$ ...
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### Has Khachiyan/Porkolob's convex integer optimization been implemented?

Khachiyan and Porkolab in 'Integer optimization on convex semialgebraic sets' gave an $O(ld^{ O(k^4)})$ algorithm to minimize a degree $d$ form with integer coefficients of binary length at most $l$ ...
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### Objective function for stochastic optimization

Stochastic Optimization problems in general deals with random variables in the 'loss function'. Incase of a Deterministic optimization problem with basic objective $\parallel Ax-b \parallel_2^2$, we ...
734 views

### Neural Networks: what's the point of learning features that don't linearly separate?

Unless I'm mistaken, deep neural networks are good for learning functions that are nonlinear in the input. In such cases, the input set is linearly inseparable, so the optimisation problem that ...
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### What is the state-of-the-art asymptotics for convex optimization?

I've got a convex program of the form: Choose $v \in \mathbb{R}^n$ to minimize $vAv^T$ subject to $O(n)$ linear contraints (including $v \ge 0$). $A$ is a square binary matrix. What algorithm gives ...
418 views

### What can be solved with semidefinite programming that can't be solved with linear programming?

I'm familiar with linear programs in that they can solve problems with linear objective functions and linear constraints. But what can semidefinite programming solve that linear programming can't? I ...
115 views

### KKT-like conditions for values close to optimal solution

The KKT conditions are necessary and sufficient conditions for problems where we maximize over a convex function subject to linear inequality and equality constraints. That is $x^*$ is an optimal ...
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### Time complexity of standard semidefinite programming solvers

I am interested in exact scaling of the ellipsoid method and interior point methods for solving SDPs. (I am not interested in algorithms like multiplicative weights updates method.)
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### Looking for algorithm (or at least name) for this optimization problem

Suppose that we have $n \geq 2$ distinct triplets $t_0, t_1, ..., t_{n-1}$ of real numbers, $t_i = (x_i, l_i, r_i)$. Define $$\Delta(s) = \max_{i, j} \left[ s (x_j - x_i) + (r_j - l_i) \right]$$ The ...
This is really a basic (undergrad) question in LP but still i would like to clarify it for myself to be sure. I have a minimization problem from the sort $\min \sum_i |x_i|$ s.t $Ax \le b$. I've seen ...