# Tag Info

18

A typical problem is MaxCut: output a cut in a graph that (approximately) maximizes the number of edges cut. Goemans and Williamson showed an SDP approximates the value of MaxCut to within a factor at least 0.878. Recently, Chan, Lee, Raghavendra, and Steurer showed that for a natural linear encoding of the MaxCut problem, all polynomial size LPs achieve ...

15

No, even if there is a finite number of feasible rank-1 matrices, the feasible region of an SDP does not have to be polyhedral. A spectrahedron you see all the time in applications is $S_n = \{X: X \succeq 0, X_{11} = \ldots = X_{nn} = 1\}$, i.e. the set of Gram matrices of $n$ unit vectors. This is, for example, the feasible region for the Goemans-...

13

It depends how the polytope is represented. In the V-polytope presentation (i.e. $P$ is given in terms of its vertices), the problem is trivial, as Tim mentioned in the comments. In the H-polytope presentation (i.e. $P$ is given as an intersection of halfspaces defined by inequalities), Brieden proved that a constant factor approximation is impossible ...

12

For many combinatorial optimization problems (for instance Max-Cut), semidefinite programming yields much stronger relaxations than the LP relaxation of IP formulations. This allows the design of approximation algorithms, and of exact algorithms which are more efficient than their linear counterparts due to the better quality of the bounds. Examples can be ...

10

To make it easier let's assume $X$ is finite, of size $n$ and associate the density of $Q$ with an $n$-dimensional vector $q$. Assume also that $q$ is everywhere positive - otherwise replace $X$ with the support of $q$. Then the conjugate is $$f^*_q(x) = \sup_p\ \langle x, p \rangle - \sum_{i = 1}^n{p_i\log(p_i/q_i)}.$$ where the supremum is over the ...

10

There is a more complicated theory of duality for SDPs that is exact: there is no 'extra condition' like Slater's condition. This is due to Ramana. (For another take on this involving SOS, see [KS12].) To be honest, I've never tried to understand these papers and would be happy if someone dumbed them down for me. One notable consequence of this work is ...

9

For the SDP in standard form $$\min\{ \mathrm{tr}(C^T X): \mathrm{tr}(A_1^T X) = b_1, \ldots, \mathrm{tr}(A_m^T X) = b_m, X \succeq 0\},$$ Slater's condition reduces to the existence of a positive definite $X\succ 0$ that satisfies the affine constraints $\mathrm{tr}(A_i^T X) = b_i$. I would guess this is satisfied for any SDP you can find in the ...

6

In Quadratic Programming is in NP, it is shown that a slight variant of this problem (QPL) is in NP. The question can be formulated as follows: does there exist a point $x \in \mathbb{R}^n$ such that $Ax \leq b$ and $x^T Q x \leq K$, where $K$ is a rational number. In the section 2 of this paper, they prove that, when the minimum exists, then there is a ...

6

Well, there are cases where LP gives you no useful information. Consider a graph $G$ with $n$ vertices, and the problem of finding a maximum independent set in $G$. The LP gives you a solution of value at least $n/2$ (give every vertex a value of $1/2$). But the optimal independent set might be of size between $1$ and $n$. On the other hand, the greedy ...

6

A famous result by Motzkin and Straus expresses the $k$-clique problem as the maximization of a quadratic function subject to a system of linear constraints. In particular, they prove: Let $G$ be a graph with vertices $1,\ldots,n$ and edge set $E$. Then $G$ contains a $k$-clique, if and only if there exist real numbers $x_1,\ldots,x_n$ that ...

5

The problem you describe is called "stochastic convex optimization in the bandit setting". This is the problem considered and solved here: http://arxiv.org/pdf/1107.1744v2.pdf They show that after $k$ queries, you can get within $O(1/\sqrt{k})$ of the minimum value of the function (ignoring a poly$(d)$ dependence on the dimension $d$ of the domain of the ...

4

The books below may be more to your liking, but in general, the texts/lecture notes are written for the use of (mainly) postgraduate students in engineering and cannot presume deep knowledge of convex analysis. Csizsar, I., and Korner, J., Information Theory: Coding Theorems for Discrete Memoryless Systems, 2nd Ed, Cambridge. Berger, T., Rate Distortion ...

4

The notion of sub-modularity you use is non-standard. Usually you consider set functions with domain $\lbrace 0, 1\rbrace^n$. But to answer your questions, the Lovász extension establishes the following relationship between sub-modularity and convexity: A set-function $F$ is sub-modular if and only if its Lovász extension $f$ is convex. For a proof see ...

4

First, notice that the first objective is a minimization problem, who's solution is a vector $x$, while the second is merely a number. The objective $\min_{x} E\left( \parallel Ax-b \parallel_2^2 \right)$ asks for the vector $x$ which best explains the data. If $A$ is stochastic, it still looks for the best $x$ which, on average, is the best one. The ...

4

Introduce variables $y_{hi}$ together with constraints $y_{hi}=\sum_j (a_{hij} x_j + b_{hj})$ for all $h$ and $i$. Introduce variables $z_h$ together with constraints $z_h\ge y_{hi}$ for all $h$ and $i$. Then minimize $\sum_h z_h$. The resulting linear program can be solved in time polynomially bounded in $\ell, m, n$ and the logarithm of the largest cost ...

4

$\chi^2$-divergence is not a Bregman divergence. I'll show it for sample size $n=1$. We would have $$(x-y)^2/x=f(x)-f(y)-f'(y)(x-y)$$ If $y=0$ and $x>0$ this says $$x=f(x)-f(0)-xf'(0),$$ $$1=\frac{f(x)-f(0)}x-f'(0).$$ Taking $x\to 0^+$ this gives the contradiction $1=0$.

3

The constraint $x_i x_j = y_{ij}$ isn't convex. Indeed, even the simpler constraint $ab = 8$ isn't convex. Let $C = \{(a,b) : ab = 8\}$. Then $(4,2),(2,4) \in C$ but $(3,3) \notin C$.

3

Sasho already gave you a yes/no answer, but here's an actual convex combination for you: If $B_\ell$ is the $k \times k$ matrix which is $1$ when $|S_i \cap S_j| \ge \ell$ and zero otherwise, then $M = \sum_{\ell=1}^{n'} \frac{1}{n'} B_\ell$.

3

If I understand your problem correctly, we can formulate it like so: given pairs $(\alpha_k,\beta_k)$ and $\Delta_0$, find $s > 0$ such that $\max_k (s\alpha_k + \beta_k) = \Delta_0$. Here is one way to solve this (this algorithm can be made more efficient). Go over all possible $k$. For each $k$, find the value of $s$ such that $s\alpha_k + \beta_k = \... 3 The problem as stated now is solvable in linear time. To see this, suppose$p\in P$is such that there are$x\in X$and$w\in W$with$p_i=x_iw_i$for all$i$. This means on the one hand that$1=\sum_{i=1}^np_i=\sum_{i=1}^nx_iw_i$, but on the other hand because of$1 = \sum_{i=1}^nx_i$and$1 = \sum_{i=1}^nw_i$we have$1 = \sum_{i=1}^n\sum_{j=1}^nx_iw_j$, ... 3 An alternative proof: Given that$\psi(p)=D_{KL}\left(p\,||q\,\right)$is closed and convex we know that$\psi^{**}(p)=\psi(p)$. One proposes$\psi^{*}(\lambda)=\log\left(\sum_{x}q(x)e^{\lambda_{x}}\right)$. It is enough to show that$\psi^{**}(p)=\sup_{\lambda}\{\lambda^{T}p-\log\left(\sum_{x}q(x)e^{\lambda_{x}}\right)\}=D_{KL}\left(p\,||q\,\right)=\...

2

Yes -- both gradient descent and the randomized weighted majority algorithm (often called multiplicative weights these days) are instantiations of the "Follow the regularized leader" framework. Gradient descent is what comes out if you regularize with the euclidean norm, and multiplicative weights is what comes out if you regularize with Shannon entropy. ...

2

Here's one possible solution: $$\nabla f(x) = \left[ \frac{\partial f(x)}{\partial x_1} \cdots \frac{\partial f(x)}{\partial x_n}\right]^T$$ The coordinate descent algorithm is exploring all the coordinate axes, so you have an estimate of $\hat{\nabla} f_i(x_k)=\frac{\partial f(x^k)}{\partial x_i}$. In particular, when $\Big| \hat{\nabla} f_i(x_k) \Big| < ... 2 In general, even approximating a global optimum of a quadratic program, the simplest case of a nonlinear program, is NP-Hard (http://web.cs.ucdavis.edu/~rogaway/papers/qp.pdf), not to mention finding an exact solution of it (http://www.sciencedirect.com/science/article/pii/002001909090100C). The polynomial-time solvability of a nonlinear program depends on ... 2 To get you answer,you may wanna look at this paper http://dept.stat.lsa.umich.edu/~gmichail/ada_final.pdf. Algorithm 2 summarizes the step you have to take to derive a boosting algorithm from any given convex loss including logitboost. 2 The first thing that comes to mind is "Smoothed Analysis" of Spielman and Teng: arxiv.org/pdf/cs/0111050.pdf. Their main result is Theorem 5.0.1, which bounds the expected (over "typical instances") runtime of a version of the Simplex algorithm by a polynomial, though the degree of the polynomial is not stated there. 2 The problem as described is not convex, due to the nonconvexity of the constraint set. However, if you were to permit a relaxation, we could write your problem as$\begin{array}{ll} \max & \sum_{p\in P} t_p\\ \text{subject to }& t_p \geq \langle x, \|p\| p \rangle\\ & t_p \geq -\langle x, \|p\| p\rangle ,\\ & \|x\|_2 \leq 1. \end{array}$... 1 There is a nice (I think....) characterization of when strong duality holds, or fails for {\em all} objective functions. We say that the semidefinite {\em system}$(P_{SD}) \,\, \sum_{i=1}^m x_i A_i \preceq B$is badly behaved if here is an objective function$c$for which the SDP$\sup c^T x \,\, s.t. \,\, \sum_{i=1}^m x_i A_i \preceq B\$ has a finite ...

1

The question is not very well defined. E.g., if the SDP has an optimal solution, then "searching" that one optimal solution is (trivially) enough to find a good approximate (in fact optimal) solution. Similarly, any algorithm that solves the SDP in finite time will find a "good approximate solution" while considering only finitely many points of the SDP. ...

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