Questions tagged [non-convex-optimization]
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46
questions
0
votes
0answers
23 views
Under what condition can we prove $\nabla_x \min_y f(x,y)=\nabla_x f(x,y^*)$ where $y^*=\arg\min_y f(x,y)$?
Let $f: \mathbb R^n\times \mathbb R^m\to \mathbb R$ be a function. I wonder under what condition can we prove $\nabla_x \min_y f(x,y)=\nabla_x f(x,y^*)$ where $y^*=\arg\min_y f(x,y)$. For example, ...
1
vote
0answers
39 views
Minimize smooth function $(x,y) \to f(x,y)$ subject to $x \perp y$
Let $V$ be a finite-dimensional real vector space (e.g space of $m \times n$ real matrices equiped with Hilbert-Schmidt inner product $(A,B) \to \mathrm{tr}(AB^\top)$, and let $f:V^2 \to \mathbb R$, $(...
0
votes
1answer
54 views
PCA, relation between the error and variance
As is known, the rank-1 PCA aims to solve the following optimization problem
$$\min_{x\in\mathbb{R}^d}\quad -x^T \Sigma x\quad\quad\quad \text{s.t.}\quad \Vert x\Vert_{2}=1,$$
where $\Sigma\in\mathbb{...
0
votes
0answers
19 views
Understanding non-convex subgradients and normal cones
I think I have a very good understanding of subgradients of convex functions and normal cones to convex sets. On the other hand, I have a lot of difficulties understanding them in the non-convex setup....
2
votes
0answers
47 views
Global minimum of sum of a non-convex and convex function, where minima of the non-convex function can be found
I'm interested in finding $\arg\min_{x \in X} (f(x) + \lVert x\rVert_2^2)$ where $X$ is a $[0,1]^n$, $f$ is Lipschitz but non-convex and we already have a procedure to find some $x^* \in \arg\min_{x\...
1
vote
1answer
56 views
Prove zero slope point is global maximum for constrained function with binomials. Without restriction, objective function is non-concave
How to prove the zero slope point is a global maximum in this non-concave program for a function with binomials?
I need to find the (global) maximum of the following constrained problem:
$$\max_{CAP} \...
0
votes
0answers
38 views
A parametrized saddle point problem with linear constraints
I am struggling to find any potential algorithm for solving a saddle point problem.
More precisely let $\mathcal{P}=\{ \mathbf{x}\in \mathbb{R}^{d}; \mathbf{A}\mathbf{x}=\mathbf{b}, \mathbf{x} \geq 0\}...
1
vote
0answers
46 views
Minimising kurtosis (non-convex). Can I use algebraic geometry or alternate methods to show uniqueness of a particular solution?
I consider a weighted sum of $n$ identically-distributed correlated random variables. The weights in the sum, $w_i$ for $i=1, 2,...,n$, satisfy $w_i>=0$ and $\sum_{i=1}^{n}w_i=1$. I am ...
3
votes
1answer
46 views
Nonconvex optimization with linear constraints
Which algorithms are suitable for solving problems of the form
$$
\min_x \lbrace f(x) \; | \; Ax \leq b \rbrace
$$
with nonconvex, differentiable obfective $f$. Unfortunately, $f$ cannot be assumed to ...
1
vote
0answers
159 views
Minimizing the Frobenius norm of a quadratic matrix expression
Given matrices $R \in \mathbb R^{m \times n}$ and $Y \in \mathbb R^{p \times n}$, where $R$ is full rank, how can I solve the following optimization problems?
$$\min_{X \in \mathbb R^{p \times m}} \| ...
0
votes
0answers
87 views
Derivation of general primal and primal-dual interior point method and their differences
I am studying interior point methods for general nonlinear optimization.
We have a problem with $n$ decision variables, $m_e$ equality constraints and $m_i$ inequality constraints.
We assume that $f(\...
0
votes
0answers
47 views
Why the result of the non-convex optimization problem will be farther and farther away from the optimal
When I try to solve a optimization problem by Riemannian stochastic variance reduced gradient algorithm(RSVRG), the formulation of problem like $\frac{1}{N}\sum_{i=1}^Nf_i(x)$ and $f_i(x)$ is a non-...
1
vote
0answers
42 views
(Iterative?) Solutions to a certain quadratic program with non-convex constraints
Let $y\in\mathbb{R}^m$, $\tau\in\mathbb{R}$ and $X\in\mathbb{R}^{m\times n}$, with $\tau>0$
I would like to efficiently solve the following problem:
Problem 1
Choose $\alpha,z\in\mathbb{R}^m,\beta\...
0
votes
0answers
102 views
Convergence of heavy-ball method for non-convex optimization
The heavy-ball method (also called gradient descent with momentum) is commonly used in optimization. The update rule can be written as:
$$x_{t+1}=x_t-\eta\nabla f(x_t)-\beta (x_t-x_{t-1})$$
Suppose $\...
2
votes
1answer
196 views
Hardness of concave minimization problem
I have an optimization problem $\underset{x}{\min} ~ c(x) - k \cdot x$ where $c(x)$ is a non-decreasing concave function with $c(0) = 0$, $x \in C \subseteq \mathbb{R}^d_{\geq 0}$. By non-decreasing, ...
2
votes
0answers
55 views
Variational forms of non-convex functions
I am trying to understand what kind of variational forms exist for non-convex functions. Alternatively, are there conjugate forms which attain strong duality? For a non-convex function $f$, I am ...
3
votes
0answers
123 views
Necessary optimality condition for quadratic programming: a solution of a constrained QAP is a solution of a LP
I have a concern about a result given by Murty in [1] and also written by Floudas and Visweswaran in [2]
They consider a QP:
\begin{array}{ll}{\min _{x} Q(x)} & {=c^{T} x+\frac{1}{2} x^{T} D ...
1
vote
1answer
176 views
A quadratic program with non-negativity constraints
Is there any closed form solution for the optimal value of the folowing optimization problem?
$$\begin{array}{ll} \text{minimize} & (\mathbf{x} - \mathbf{y})^{\mathrm{T}}\mathbf{B}(\mathbf{x} - \...
5
votes
1answer
281 views
If $\ell_0$ regularization can be done via the proximal operator, why are people still using LASSO?
I have just learned that a general framework in constrained optimization is called "proximal gradient optimization". It is interesting that the $\ell_0$ "norm" is also associated with a proximal ...
5
votes
1answer
419 views
Solving a linear program, but over the unit sphere
I want to solve a linear program but with a subset of the variables taken from a unit sphere.
That is, given fixed $\textbf{c} \in \mathbb{R}^{n}$, $\textbf{A} \in \mathbb{R}^{m \times (n+k)}$,
I want ...
2
votes
0answers
150 views
Quartic optimization problem over the unit Euclidean sphere
I want to solve following optimization problem in $x \in \mathbb R^n$.
$$\begin{array}{ll} \text{maximize} & \displaystyle\sum_i(x M_i x^T)^2\\ \text{subject to} & \|x\|_2 = 1\end{array}$$
...
2
votes
0answers
54 views
Solving Mixed-Integer Non-Linear Optimization Problem
I would like to solve the following optimization problem:
\begin{array}{ll}
\underset{x_{i}\geq0,\, \pi_{i}\in\{0,1\}}{\text{minimize}} & \displaystyle\sum_{i=1}^n x_i\\
\text{subject to} & ...
3
votes
2answers
170 views
Program to solve Optimization Problem
I have an optimization problem, this problem has linear constraints and nonlinear constraints. I solved the linear part by MATLAB but the nonlinear constraints I could not solve it. I downloaded ...
7
votes
2answers
354 views
Proving an infinite norm minimization problem has finite support (non-convex p-norms)
Consider an optimization problem over infinite variables:
$$
\begin{align}
\min_{x}~& {\left\lVert{x}\right\rVert }_p
\\
\text{s.t}~& \left\langle x, a_n\right\rangle \ge 1~,~\forall n=1,\...
4
votes
1answer
196 views
Optimizing a multivariate symmetric (permutation-invariant) function
Let $\ell$ and $d$ be two integers such that $\ell \le d$.
I would like to find the global maxima of the following symmetric function $f\colon (0,1]^n \to \mathbb{R}$,
$$f(x_1, \ldots, x_n) := \sum_{\...
1
vote
2answers
2k views
Maximizing a convex function with a convex constraint
Given a convex function $f : \mathbb{R}^n \to [0,\infty)$, the objective is to find the farthest point in the level set $\left\lbrace x \in \mathbb{R}^n \mid f(x) \leq 1\right\rbrace$ (Assuming that ...
2
votes
1answer
79 views
Linear optimization with one positive definite quadratic equality condition in P?
I have the following minimization problem in $z \in \mathbb R^n$, which contains $x_1, \dots, x_t, y \in \mathbb R$.
$$\begin{array}{ll} \text{minimize} & y\\ \text{subject to} & xQx'= y\\ &...
0
votes
0answers
143 views
non-convex optimization with constraint
I have a special non-convex optimization problem:
$\min / \max \ f(x) + g(x) + h(x)$,
subject to $| g(x) - h(x)| < \varepsilon$,
where $f(x)$ is non-convex, but both $g(x)$ and $h(x)$ are ...
1
vote
1answer
309 views
Maximizing quadratic form subject to inequality constraints [closed]
Given a $n \times n$ symmetric matrix $\rm S$, solve the optimization problem in $n \times k$ (where $n \geq k$) matrix $\rm X$
$$\begin{array}{ll} \text{maximize} & \mbox{tr} \left( \mathrm X^\...
7
votes
2answers
364 views
Eigenvalue problem with two quadratic constraints
I would like to solve the following problem:
$$\begin{array}{ll} \text{minimize} & \mathbf{x}^T \mathbf{A} \mathbf{x}\\ \text{subject to} & \mathbf{x}^T\mathbf{B}\mathbf{x} = 0\\ & \...
3
votes
1answer
86 views
Why to multiply the penalty by $n$ in the penalized least squares and likelihood?
In the SCAD paper by Fan and Li (2001), there exist two forms of penalized least squares as follows:
$$\frac{1}{2}\left \| y-X\beta \right \|^2+\lambda \sum_{j=1}^{d}p_j (\left | \beta _j \right |),$$
...
1
vote
1answer
277 views
Are there any solvers to Chance Constrained Programming Problems
I'm trying to solve a chance constrained programming (CCP) problem
$\min_x f_0(x, \xi), \text{ such that } \mathbb{P} ( f_i(x, \xi) \ge \alpha_i ) \le \epsilon_i, \text{ where } i = 1,2,\cdots, m$
...
5
votes
1answer
318 views
Maximize the determinant of Boolean combinations of positive definite matrices
I have the following optimization problem.
$$\begin{array}{ll} \text{maximize} & \det \left(\sum^n_{i=1}z_i W_i \right)\\ \text{subject to} & \sum_{i=1}^n z_i = N\\ & z_i
\in \{0,1\}\end{...
4
votes
1answer
3k views
Can you give me good examples of non-convex functions that are problematic for optimization?
I want to test my extended gradient descent algorithm, whose aim is to handle non-convex problems better. Can you give me some examples of non-convex functions that are hard to minimize via gradient ...
1
vote
0answers
243 views
Is this QCQP convex or nonconvex?
\begin{equation}
\begin{split}
\min_{x\in \mathbb{R}^n}\:f(x)=(1/2)x^{T}Q_0x+c_0^T x
\end{split}
\end{equation}
s.t.
$$
g_i(x)=\frac{1}{2}x^T Q_ix-lmax_i\leq0,i\in\{1,...,m/2\}
$$
$$
g_i(x)=\frac{...
5
votes
2answers
2k views
Quadratically constrained linear program (QCLP) over $x$ with the linear constraint $x = Az$
I have a problem that looks very much like a norm-constrained linear program, but with an extra constraint that is unusual for me. The problem is the following. Given a matrix $A$ and a vector $w$,
$$...
12
votes
2answers
4k views
Linearly constrained eigenvalue problem
Suppose I'd like to:
\begin{align}
\mathop{\text{min}}_\mathbf{x} && \mathbf{x}^T\mathbf{A}\mathbf{x} \\
\text{subject to:} && \mathbf{x}^T \mathbf{M} \mathbf{x} = 1\\
&& \...
5
votes
1answer
1k views
Maximizing quadratic form on the hypercube
I want to maximize a quadratic form $\mathbf x^T\mathbf Q\mathbf x$ and also want to find out which vector $\mathbf x$ maximizes the quadratic form when
$\mathbf Q$ is an $n\times n$ positive ...
3
votes
1answer
683 views
Nonconvex optimization problem
I have a nonconvex optimization problem with a linear objective function, a set of linear constraints and a set of nonlinear, non-convex constraints. Is this problem NP-hard? If so, how can I prove ...
3
votes
1answer
398 views
Is the feasibility of a system of non-convex quadratic equations and inequations decidable?
I would like to know whether the following problem is decidable.
Given the following system in $x \in [0,1]^n$
$$x^T Q_i x + r_i = 0 \mbox{ for } i = 1, ..., k$$
$$x^T Q_j x + r_j \neq 0 \mbox{ ...
2
votes
1answer
161 views
Non-convex quadratic optimization
I would like to optimize the following system:
$$\min_{q,\|q\|=1} \sum_i^n |q^T M_i q|$$
More details:
the size of the unknown vector $q$ is $4 \times 1$,
$M_i$ is a matrix of size $4\times 4$. It ...
1
vote
1answer
611 views
A non-convex quadratically constrained quadratic program
$$\begin{array}{ll} \text{minimize} & \beta^{T} A \beta\\ \text{subject to} & \beta^{T} C \beta=1\\ & \beta \geqslant 0\end{array}$$
where $A, C\in \mathbb{R}^{M\times M}$ and $\beta \in ...
2
votes
1answer
1k views
Can one maximize the spectral norm of a matrix via semidefinite programming?
Consider the following optimization problem:
Maximize $\|X\|_2$, subject to $X$ being Hermitian (or symmetric) and a bunch of semidefinite constraints on $X$. Here, $\|X\|_2$ is the spectral norm of ...
2
votes
3answers
2k views
Solving a non-convex quadratically-constrained quadratic program
I have the following quadratic optimization problem: $\min_{\vec{x}} |\vec{x}|^2$ subject to $\vec{x}^T G_j \vec{x} \geq 1$, $j = 1 \ldots m$, where the $G_j$ are positive semidefinite. $|\vec{x}|$ is ...
2
votes
2answers
3k views
Projected gradient descent for non-convex optimization problems
My question is in regards to the minimization of a convex function where the feasible set of solutions is non-convex. Can projected gradient descent (PGD) be used here to obtain a stationary solution?
...
48
votes
7answers
22k views
Is all non-convex optimization heuristic?
Convex Optimization is a mathematically rigorous and well-studied field. In linear programming a whole host of tractable methods give your global optimums in lightning fast times. Quadratic ...
