Questions tagged [non-convex-optimization]
The non-convex-optimization tag has no usage guidance.
22
questions with no upvoted or accepted answers
3
votes
0
answers
152
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 ...
- 415
2
votes
0
answers
387
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\...
2
votes
0
answers
86
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 ...
- 31
2
votes
0
answers
224
views
Quartic optimization problem over the unit Euclidean sphere
I want to solve following optimization problem in $x \in \mathbb R^n$.
$$\begin{array}{ll} \text{maximize} & \displaystyle\sum_i(x M_i x^T)^2\\ \text{subject to} & \|x\|_2 = 1\end{array}$$
...
- 415
2
votes
0
answers
67
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} & ...
- 21
1
vote
0
answers
58
views
Under which condition, such that all second-order critical points satisfy $\sum_j\cos(\theta_i-\theta_j)>0$ for all $i\in[n]$?
Consider the following non-convex function
$$E(\theta):=-\sum_{i,j}A_{ij}\cos(\theta_i-\theta_j)$$
where $A$ is a symmetric, diagonal-free matrix whose non-diagonal element are $\pm 1$. In other words,...
- 39
1
vote
0
answers
100
views
Maximisation of a convex (quadratic) function
This post is a continuation of A variant of (discrete) optimal transport problem
For $\alpha=(\alpha_1,\ldots,\alpha_m)\subset\mathbb R^m_+$, $\beta=(\beta_1,\ldots,\beta_n)\subset\mathbb R^n_+$ and $...
- 975
1
vote
0
answers
123
views
Can I solve this quadratic program "fast"?
We are given a matrix $D \in \mathbb{Z}^{C \times C}$ of non-negative entries, an integer $k \geq 1$ and we need to maximize the quadratic form $x^T D x$ under some simple constraints. For all ...
- 75
1
vote
0
answers
65
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 $f:V^2 \to \mathbb R$, $(...
- 6,726
1
vote
0
answers
102
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 ...
- 173
1
vote
0
answers
455
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?
$$\min_{X \in \mathbb R^{p \times m}} \| ...
- 11
1
vote
0
answers
61
views
(Iterative?) Solutions to a certain quadratic program with non-convex constraints
Let $y\in\mathbb{R}^m$, $\tau\in\mathbb{R}$ and $X\in\mathbb{R}^{m\times n}$, with $\tau>0$
I would like to efficiently solve the following problem:
Problem 1
Choose $\alpha,z\in\mathbb{R}^m,\beta\...
- 183
1
vote
0
answers
389
views
Is this QCQP convex or nonconvex?
\begin{equation}
\begin{split}
\min_{x\in \mathbb{R}^n}\:f(x)=(1/2)x^{T}Q_0x+c_0^T x
\end{split}
\end{equation}
s.t.
$$
g_i(x)=\frac{1}{2}x^T Q_ix-lmax_i\leq0,i\in\{1,...,m/2\}
$$
$$
g_i(x)=\frac{...
- 11
0
votes
0
answers
32
views
Generalizations of Berge's maximum theorem
I have a parameterized optimization problem
\begin{eqnarray}
\max_{x\in D(\theta)} f(x,\theta).
\end{eqnarray}
Assumptions of the standard Berge's maximum theorem are satisfied, so the value function $...
0
votes
0
answers
30
views
Efficient algorithms to find the global minimum of a non-convex quadratically-constrained quadratic program
I am working on a problem involving a non-convex quadratically-constrained quadratic program and am seeking efficient algorithms to find its global minimum. The problem is structured as follows:
Fix ...
0
votes
0
answers
75
views
How to solve mckp (multiple-choice knapsack problem) problem with non-linear constraint
How to solve the below optimization problem? $P$ is a probability matrix, $0\le P_{ij}\le 1$. Are there any developed tools to solve this? Thanks a lot.
\begin{equation*}
\begin{aligned}
&\...
0
votes
0
answers
47
views
Trying to transform a minimization problem to a saddle point problem for the primal–dual algorithm
I’m reading about a problem, and the author goes from a classical minimization problem to a saddle point problem in order to perform a primal–dual algorithm on it [1].
However, It’s my first problem ...
- 1
0
votes
1
answer
104
views
An optimization problem with variables on the exponential of a complex number
$$\min_t \quad\operatorname{Re} \sum\limits_{i = 1}^N {\left( {{e^{ - j2\pi {f_i}t}}{r_i}} \right)}, $$where $\operatorname{Re}$ refers to get the real part of a complex number, $\{f_i\}$ is an ...
0
votes
0
answers
54
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
0
votes
0
answers
284
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 $\...
- 601
0
votes
0
answers
171
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 ...
- 43
0
votes
1
answer
152
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} \...
- 49