# Questions tagged [semidefinite-programming]

Semidefinite programming can be regarded as an extension of linear programming. In a semidefinite program, the goal is to optimize a linear function over the intersection of the cone of positive semidefinite matrices with some affine space.

91
questions

0
votes

0
answers

54
views

### Minimizing the Spectral Norm of the Hadamard Product of a Quadratic Form Using CVX

I am trying to use CVX to minimize the spectral norm of the Hadamard product of two matrices, one of which is in quadratic form. Specifically, I am trying to minimize $\|{\bf A} \odot {\bf XX}^H\|_2$, ...

1
vote

0
answers

65
views

### Reference request: finding entries that prevent matrix from being correlation matrix

I am currently doing some research with a quantitative finance firm and my supervisor has raised an interesting question that shows up a lot with their clients: quite often, clients will want to do ...

0
votes

0
answers

59
views

### Condition to show $\{ U \in \mathbb{R}^{n \times p}|\mathscr{A}(UU^{\top}) = b \}$ is (is not) a manifold

Consider $\mathscr{A}: S^{n\times n} \to \mathbb{R}^{m}$, $b \in \mathbb{R}^{m}$, I would like to know when $\mathscr{M}:=\{ U \in \mathbb{R}^{n \times p}|\mathscr{A}(UU^{\top}) = b \}$ is a manifold. ...

1
vote

0
answers

186
views

### Interpreting positive semidefinite matrix as a graph

Given any symmetric matrix $S \in \mathbb{R}^{n \times n}$, if $S \succeq 0$, is there a way to encode $S$ into a graph such that it takes into account the positive semidefinite constraint, and ...

1
vote

1
answer

84
views

### Norm bound in simultaneous stability to semidefinite program

In the context of robust control, I remember hearing that the two following problems are equivalent.
Find $P \succ 0$, such that $A P + P A^{\top} \prec 0$ for all $A \in \mathscr{A}$ where $$\...

0
votes

0
answers

23
views

### Dual semi-definite programming formulation of the quantum adversary method

I'm reading about the quantum adversary method and its semi-definite program formulation. It is not clear to me how to arrive to the dual SDP problem as usually found (e.g. here page 101). Given a ...

1
vote

1
answer

181
views

### Perturbation of positive semidefinite matrix

Consider an $n\times n$ matrix $A$ that is positive semidefinite and has rank $n-1$, so there exists exactly one eigenvector $v$ such that $Av=0$. Let now $B$ be a symmetric matrix such that $v^TBv=0$....

3
votes

1
answer

178
views

### Positive-definite block matrix with constant block sums

Given two natural numbers $n$ and $m$, suppose that $A$ is an $nm \times nm$ real nonnegative matrix. Seeing $A$ as a block matrix where each block has size $m\times m$, suppose that the sum of the ...

1
vote

0
answers

177
views

### Fastest algorithm for finding the closest semi-definite matrix?

Given a real-valued, symmetric matrix $A \in \mathbb{R}^{n \times n}$, I'm interested in finding the closest positive semi-definite matrix $X^*\in \mathbb{R}^{n \times n}$:
$$
X^* = \mathop{\text{...

0
votes

0
answers

69
views

### Feasibility of a polynomial system of equalities and inequalities

Consider a system of the form $f_i(x) = 0$ and $g_j(x) \ge 0$ ,where $f_i,i=1,\dots,r$ and $g_j,j=1,\dots,s$ are polynomials in real unknowns $x_i,i=1,\dots,n$ with rational coefficients.
Is there a ...

1
vote

0
answers

69
views

### If $\hat{D}$ minimizes trace over all $D+ B \succeq 0$, then is $\hat{D}_{ii} \leq \sum_{j} |B_{ij}|$ for each $i$?

Let $A$ be an $n \times n$ real matrix and let $B$ be the block bipartite matrix
$$B = \begin{bmatrix} 0&A \\
A^{T}&0 \end{bmatrix}$$
Let $\hat{D}$ be a solution to the SDP that minimizes $tr(...

6
votes

0
answers

124
views

### Minimizing $\det(D)$ for all diagonal matrices $D$ that satisfy $D+B \succeq 0$

Let $A$ be an $n \times n$ real matrix and let $B$ be the block bipartite matrix
$$B = \begin{bmatrix} 0&A \\
A^{T}&0 \end{bmatrix}$$
I came across the following optimization problem, which ...

1
vote

2
answers

63
views

### Monotonicity of kernel matrices with respect to hyperparameters

Let $\mathcal{X}$ be some nice space, let $\Phi$ be some ordered space, and let $K :\mathcal{X} \times \mathcal{X} \times \Phi \to \mathbf{R}$ be a positive-semidefinite kernel indexed by a ...

0
votes

0
answers

126
views

### Literature request: proving or disproving convexity of the optimal value function of semidefinite program (SDP) or convex optimization in general

Suppose I have a function $f:\mathbb{R}\rightarrow \mathbb{R}$ defined as the following parametric optimization problem:
$$f(p) = \inf_xf_0(x) \quad \text{subject to } \quad G(x,p)\leq 0,$$
where ...

2
votes

1
answer

224
views

### Solving linear programming without solving linear programming

Let $v_1, \cdots, v_n$ be vectors in $\mathbb R^k$, and let $M$ be the Gram matrix of them.
It's possible to determine from $M$ and $k$ whether the only vector that has nonnegative inner product with ...

2
votes

0
answers

112
views

### Modified quadratic assignment problem

Let $Y,Z$ be $n\times k$ matrices and assume all columns have been standardized such that their means are zero and variances 1. I seek an $n\times n$ permutation matrix $P$ such that
$$\left\Vert Y^{T}...

0
votes

0
answers

152
views

### Double summation of matrices as constraints in convex optimization in CVX

I want to implement the following optimization problem from the following paper Randomized Gossip Algorithms, Page 10 Eq 53:
\begin{align}
\text{minimize} &\qquad s\\
\text{subject to} & \...

2
votes

0
answers

56
views

### A variant of the elliptope relaxation

Given a p.s.d. matrix $A$, one may want to find:
$$
\max_x x^t A x \mbox{ such that } x \mbox{ has entries }1 \mbox{ or } {-1}.
$$
This hard problem has a well known relaxation based on the so called ...

0
votes

0
answers

125
views

### On least-squares with positive semidefinite constraints

Given real symmetric matrix $\mathbf{R} \in \mathbb{S}^{n\times n}$ and matrices $\mathbf{X}_n, \mathbf{X}_{n-1} \in \mathbb{R}^{n \times m}$,
$$\begin{array}{ll} \underset{\mathbf{A} \in \mathbb{R}^{...

3
votes

1
answer

195
views

### Relaxations for the spectral norm maximization problem

Optimizing the spectral norm of some positive semidefinite matrix $A(x) \in S^{n}$, w.r.t. a list of variables $x \in \mathbb{R}^d$ and semidefinite constraints is, in general, a nonconvex problem (...

2
votes

0
answers

169
views

### Certificates of connectivity of basic semi-algebraic sets

Given real polynomials $p_1, \ldots, p_n \in {\mathbb R}[x_1, \ldots, x_d]$, consider the closed basic semi-algebraic set $S \subseteq {\mathbb R}^d$ given by $$S := \{x \in {\mathbb R}^d : p_i(x) \...

4
votes

3
answers

245
views

### When does a finite metric induce a matrix norm?

If I have a metric $d(\cdot,\cdot)$ on the set $\{1,\dots,n\}$, are there well-known necessary or sufficient conditions for the existence of a matrix norm $Q$ that induces that metric on the unit ...

4
votes

1
answer

186
views

### What is the convex cone generated by the pair of rank 1 matrix and its eigenvector?

I'd like to know what is the convex cone generated by $\left\{ (h h^T, h) : h \in \Bbb R^{d\times1} \right\}$. It is known that $$\mathrm{cone} \left\{h h^T : h \in \Bbb R^{d \times1} \right\} = S_+^d$...

2
votes

0
answers

150
views

### Three-constraint homogeneous QCQP

Consider the homogeneous quadratically constrained quadratic program,
$$\min_{u^T u =1} u^T A_1 u$$
$$\textrm{subject to}\quad u^T A_2 u = 0,\quad u^T A_3 u = 0$$
This problem is particularly studied ...

1
vote

0
answers

37
views

### Solution to dynamic program-type recursion

I have the following dynamic programming principle-type problem.
Suppose that we are given a sequence $\beta_1,\dots,\beta_n\in (0,\infty)$, some target $y\in (0,\infty)$ with $y>\sum_{t=1}^N \...

2
votes

1
answer

58
views

### Convexity of a positive definite objective with min(x,y)-nonlinearity

I have derived an optimization objective of the form
$$
f(x) = \sum_{i,j} C_{ij}\min(x_i, x_j), s.t. g(x) \geq 0
$$
where $C \in \mathcal{R}^{N \times N}$ is a positive definite matrix, and $x \in \...

1
vote

0
answers

68
views

### Is there an efficient way to do semidefinite programming with a Lyapunov equation constraint?

I am trying to numerically solve semidefinite programs of the form
$$\begin{array}{ll} \underset{X,Y}{\text{minimize}} & \operatorname{tr}(AX)\\ \text{subject to} & BY + YB = X\\ & X, Y \...

0
votes

2
answers

1k
views

### Matrix norm minimization and matrix inner product

One of the famous problem in SDP is the matrix norm minimization (see S. Boyd, Convex Optimization, p. 170).
Consider:
\begin{equation}\label{eq:Lasse}
\begin{aligned}
&\min_{\mathbf{x}}
& &...

3
votes

0
answers

113
views

### Matrix inequality $a X \succeq arcsin(X)$ for some $a > 0$

Let $X \in S^{n}_{+}$ be a positive semi-definite matrix with $X_{ii} = 1$ for all $i \leq n$ (thus $X$ is a correlation matrix).
Since $X$ is positive semi-definite, we have $|X_{ij}| \leq 1$ for any ...

3
votes

0
answers

67
views

### Non-negative bivariate polynomials in a rectangle

I have been working on non-negative univariate polynomials and I found the following equivalent relationship to check if a polynomial is non-negative or not:
The polynomial $g(x) = \sum_{r=0}^k y_rx^...

1
vote

0
answers

49
views

### Minimum operator that exceeds others (in a PSD, linear matrix inequality, sense)

Given a collection of $n$ matrices $A_i$, we could ask for the $B$ such that:
$$\textrm{Minimize: }\quad \textrm{Tr}[B]$$
$$\textrm{Such that: }\forall_i\, B \succeq A_i$$
Here $\succeq$ is in the ...

1
vote

0
answers

68
views

### Fundamental regions in convex programming

In linear programming, the fundamental regions are polyhedra, since those are the intersection of half-spaces defined by linear inequalities. In semidefinite programming, the fundamental regions are ...

1
vote

0
answers

80
views

### SDP relaxation vs. Monte Carlo for MaxCut: which one performs better?

the Goemans Williamson SDP relaxation of the MAXCUT problem famously gives a polynomial approximation ratio of .87856 for the MAXCUT on regular graphs.
Another popular approach to obtain efficient ...

1
vote

0
answers

81
views

### What is the relation between different generalizations of linear programming?

Linear programming subsumed by each of
Semidefinite programming (SDP)
Convex programming (CXP)
SOS programming (SSP)
Is there any relation between each pair in the three?
Are all three equivalent in ...

1
vote

0
answers

70
views

### Reference request for linear matrix inequality with PSD matrices

In literature, people say a spectrahedron is the following set
$$\left\{x \in \mathbb{R}^d : x_1 A_1 + \cdots + x_d A_d \geq B \right\}$$
where $\geq$ is in the positive semidefinite sense. Is there a ...

2
votes

0
answers

166
views

### Representations in Archimedean quadratic modules

Let $\mathbb R [X] = \mathbb R [X_1,\dots,X_n]$ and $\Sigma[X] = \big\{ \, f \in \mathbb R[X] \mid \exists r \in \mathbb N, \ g_i \in \mathbb R[X] \colon f = g_1^2 + \dots + g_r^2 \,\big\}$ denote ...

4
votes

1
answer

244
views

### Max-norm projection of a Hermitian matrix onto the set of positive semidefinite matrices

For a given Hermitian matrix $A$ (i.e. complex matrix with $A_{ij}^{\ast}=A_{ji}$) find its max-norm projection onto the set of complex positive semi-definite matrices:
$$\Pi(A)=\mathrm{argmin}_{M\...

1
vote

0
answers

64
views

### Linear algebra - For symmetric matrix X $\in S^n$, prove the $a^T X a$ = $\det X \det(X_{n-1})$ , where $a_i$ = $(-1)^i M_{in} $ [closed]

Suppose we have a symmetric matrix X$\in S^n$, and $X_k$ denotes the submatrix consists of first $k$ rows and columns of X. If $\det X < 0$, but $\det X_1, ..., \det X_{n-1} > 0$. Let $a_i=(-1)^...

1
vote

1
answer

2k
views

### Matrix Completion SDP Relaxation and Duality

I am studying the matrix Completion problem, as well as its SDP relaxation. However, I am having trouble deriving the final SDP form of the matrix completion problem. I will give some background, ...

1
vote

0
answers

99
views

### Are those two Sum-Of-Squares approach for unconstrained polynomial optimization related?

I found 2 approaches to solve an unconstrained polynomial optimization problem using the Lasserre / SOS hierarchy:
$$
\inf_{x\in\mathbb{R}^n}\quad p(x),
$$
where $p$ is a polynomial of even degree ...

2
votes

0
answers

136
views

### Generalization of Farkas' Lemma to Hermitian Matrices

I recently stumbled upon a well-known version of Farkas' Lemma which, roughly speaking, I would like to generalize from real vectors to hermitian matrices, as it seems promising for something else I ...

5
votes

2
answers

507
views

### Exactness of the semidefinite programming (SDP) relaxation of maximum cut (Max-Cut)

Currently, what conditions are known to be sufficient for the SDP relaxation of Max-Cut to be exact?

3
votes

0
answers

170
views

### Uniqueness of projection under spectral norm

I am considering
$$
\min_{M\in \mathcal{M}} \|X - M\|:=x \neq 0,
$$
where $X$, $M$ are $m\times n$ matrices, $\|\cdot\|$ is spectral norm and $\mathcal{M}$ is a matrix subspace. I wonder to what ...

2
votes

1
answer

182
views

### SDP representation of ideal polynomials for positivstellensatz refutations

If we want to certify the nonexistence of real solutions to a polynomial system of equations, i.e.
$$ S = \{ x\in \mathbb{R}^n \ | \ h_i (x) = 0, \ i=1,\dots,t \} = \emptyset, $$
we can produce a ...

4
votes

1
answer

216
views

### Convex Hull of Outer Products of (Normalised) Nonnegative Vectors

If I define $\mathcal{A} = \{ xx^T : x \in \mathbb{R}^d, \| x \|_2 \leqslant 1 \}$, then (assuming I recall correctly) it is known that the convex hull of $\mathcal{A}$ is given by
\begin{align}
\...

2
votes

2
answers

163
views

### Subspaces of real $n \times n$ matrices of dimension $O(n)$ [closed]

The set of real $n \times n$ matrices forms a vector space over the reals. Given any set $S$ of $n \times n$ matrices, there is a basis $S' \subseteq S$ of size at most $n^2$ such that any $x \in S \...

4
votes

1
answer

533
views

### Advantages of hyperbolic programming over semidefinite programming?

What are the advantages of a hyperbolic program over a semi definite program? SDPs can be used to represent a wide variety of algebraic constraints. Are there constraints that can be represented in a ...

12
votes

2
answers

765
views

### A (linear) optimization problem subject to (linear) matrix inequality constraints

Let $A \in \mathbb{R}^{n \times n}$ be a Hurwitz matrix, i.e. $A$ satisfies $\mathrm{Re}\,\lambda_i< 0$, where $\{\lambda_i\}_{i=1}^n$ is the set of eigenvalues of $A$. Suppose that the trace of $A$...

3
votes

0
answers

4k
views

### About product of PSD matrices

In Theorem 3 in this paper, https://core.ac.uk/download/pdf/82822897.pdf, ``On a product of positive semidefinite matrices, A.R. Meenakshi, C. Rajian, Linear Algebra and its Applications, Volume 295, ...

7
votes

1
answer

2k
views

### Solving system of bilinear equations

Consider a collection of $m$ matrices $A_i$ of size $n\times n$, and a vector $b$ of size $m$. I want to solve the bilinear system
$$\left\{ x^T A_i y = b_i : i = 1,\dots,m \right\}$$
in variables $x,...