# Tagged Questions

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### Distributed Convex Optimization Algorithm

Consider the convex optimization problem $$\min_{x_1, \cdots, x_N, y} \sum_{i=1}^{N} f_i(x_i,y)$$ $$\text{subject to: } x_i \in X_i \ \ \forall i, \ \ y \in Y, \ \ y = \sum_{i=1}^{N} x_i$$ ...
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### Reentrant constraints in active set algorithm?

Problem definition Supposing you're trying to solve a quadratic program: $$\min_x f(x) = \frac{1}{2}x^T Q x + c^T x \\ \mbox{s.t} \, \; A x \ge b$$ Where Q is square ($n$x$n$), positive semi ...
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### Quadratic programming with linear equality constraints in Matlab [on hold]

I have to identify an ARX under some linear constraints, this means that I have a quadratic programming with linear equality constraints problem. One way is to use the following equations in the ...
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### Augmented Lagrangian

Consider the following equality constraint minimization problem: minimize $\text{ }f(x)$ subject to $Ax=b$ Its Lagrangian is then: $L(x,y) = f(x) + y^T(Ax-b)$ We can use then gradient ascent to ...
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### Optimizing an expression containing sum of square roots of squared terms

For optimization problems involving square root, it is common to optimize the squared expression instead of that containing the square root. What if we have sum of squared expressions ? Consider the ...
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### Is this optimization problem solvable?

I have the following optimization problem: $$\min_\mathbf{x}\|\mathbf{a+Bx}\|^2 ~~\text{s.t}~~ \|\mathbf{y+Ax}\|_\infty \leq \beta\|\mathbf{y}\|_\infty ~~,~~ \|\mathbf{x}\|^2 \leq \alpha^2$$ where ...
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### How to solve this optimization problem? (may be gradient descent?)

I have the following optimization problem. $$\operatorname*{argmax}_{w} \|(1-w)\boldsymbol{X} -w\boldsymbol{Y}\|^2 \\ s.t. \quad 0<w<1$$ How can I find the solution of this problem? May be ...
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### Alternating Direction Method of Multipliers (ADMM) application

$\newcommand{\argmin}{\operatorname{argmin}}$ Recall, that ADMM algorithm solves the problem of the form: $\min \text{ } f(X) + g(Z)$ $\text{s.t. } AX + BZ = C$ where $X$, $Z$ and $C$ are real ...
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### Knuth's Sandwich Theorem: requesting proof clarification

The question is about F6 of Section 8 ("Elementary facts about cones") in Donald Knuth's Sandwich Theorem (http://arxiv.org/pdf/math/9312214.pdf). He claims to prove $(A \cap B)^* = A^* + B^*$ when ...
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### How to deal with non-existent derivatives in Lagrangian?

I am stucked at a detail in a constrained optimization problem: Question Assume that the objective function is continuous on its domain $D$, but at some points $Z \subseteq D$ it is not ...
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### Coordinate descent with constraints

Coordinate descent is a powerful method for solving optimization problems like $$\min_x \tfrac{1}{2}x^T A x + b^T x + \lambda ||x||_1$$ where $A$ is symmetric and positive definite, $\lambda>0$ ...
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### Constraint optimization with lagrangian

I am having trouble regarding the general steps one needs to take in order to solve an constraint optimization using Lagrangian. More specifically, I want to maximize objective equation $f(x,y,z,w)$ ...
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### How exactly do I prove that I find the maximum of the function

I am currently trying to maximize an objective function $f(a,b,c,d,e)$ over the variable $b$ only. By taking the derviative of f over b, setting it to zero, I can solve b in terms of the other 4 ...
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### Strong duality: When does the optimal primal variable coincide with the primal variable giving the dual function.

I'm considering the inequality-constrained optimization problem of finding $$x^{\star} = \arg \min_{x} f(x) \;\; \text{s.t.} \;\; h(x) \le 0$$ which is assumed to have a unique minimizer. The ...
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### L1 regularized SVM in Matlab

Minimizing the following SVM formulation \begin{align} \arg\min_{\mathbf{w}}\frac{1}{2}\|\mathbf{w}\|^2_2 \\ \text{subject to } \quad y_i(\mathbf{w}\cdot\mathbf{x_i}) \ge 1 \end{align} can be done ...
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### Reference for gradient descent with unit norm constraint

I faced a non-convex optimization problem with unit norm constraint. I can solve the problem using the gradient descent method and the projection of the gradient onto the tangent plane as in @joriki ...
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### multi-objective optimization

I am currently encounterring a optimization problem. The goal is optimize an objective function A and B at the same time. But the problem is that optmizing A will almost always tradoff with B, such ...
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### Origin of Slater's condition

I've been looking all over the internet to answer this question: Slater's condition is a commonly used to certify that strong duality holds in a convex optimization problem. Although used in many ...
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### What numerical methods are known to solve $L_1$ regularized quadratic programming problems?

What numerical methods are suitable to solve the following problem $$\min_x \tfrac{1}{2}x^T A x + b^Tx + \lambda ||x||_1$$ where $x,b\in\mathbf{R}^n$, and $A\in \mathbf{R}^{n\times n}$ is positive ...
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### Strong convexity of a function with cases

Given a set $S = \{x_1,\dotsc,x_n\} \subset \mathbb{R}$, is the function \begin{align} f&: (0,\infty) \to \mathbb{R} \\ f&(p) = 2p^2 + \frac{1}{n}\sum_{i=1}^n \max(0, -p^2-x_i) \end{align} ...
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### dual value of a linear constraint

Assume a minimization problem. The dual of an inequality '<' constraint is the marginal improvement in the objective function (ie marginal reduction) by marginally increasing the right-hand-side ...
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