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Questions tagged [nonconvex]

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How to solve this nonconvex problem in python

I have the following problem to solve minimize $$\sum_{i=1}^I\sum_{k=1}^Kx_{i,k}.$$ The constraints are as follows: $$\sum_{i=1}^I\sum_{j=1}^J\ln(c+x_{i,k}A_{i,j,k})\geqslant B_k,\forall k,$$ and $$\...
zdm's user avatar
  • 121
0 votes
0 answers
49 views

Convex Optimization: Finding maximally different solution

I am using cvxpy to maximize a function f(x) given the constraints -1 <= x <= 1. Let's call the solution x0. Now, I define a region around the optimal value f(x0) and want to find another ...
Neo's user avatar
  • 1
2 votes
1 answer
123 views

How to formulate a convex expression to minimize the difference between Frobenius norm of a positive semidefinite matrix and a positive value

So what I am trying to do is to minimize the distance between the Frobenius norm of a PSD matrix and a real positive value, which can be formulated as $$\min \left|\|\textbf{P}\|_F - J\right|^2$$ ...
tyrela's user avatar
  • 133
1 vote
0 answers
66 views

min(f(x)) is convex or concave based on type of f(x)

i have f(x) that is concave function. My question is g=min(f(x)) is concave or convex? And max(g) is concave or convex? there is a theorem for this?
Maria's user avatar
  • 11
2 votes
0 answers
34 views

Calculating a submanifold of minimal values

I have a (numerical, noisy) function $f$ that I'd like to optimize (in 3 < dimension < 10). The function $f$ has a fairly prominent valley/ravine structure due to physical symmetries present in ...
Nathanael Schilling's user avatar
2 votes
0 answers
128 views

Efficient solver of a Integer programming

I am solving an Integer programming using MATLAB, yet the efficiency is low. Here is the problem: Suppose $v$ is a $N \times 1$ vector. For $v_i \in v$, $v_i \in \{0,1\}$. $D$ is a 0-1 matrix, which ...
Bruno's user avatar
  • 21
2 votes
0 answers
99 views

Black box optimization

I have a simulation which gives a scalar result depending on the choice of some continuous design variables. I am trying to minimize the output of the simulation. As a first step, I want to study the ...
Karl Maroun's user avatar
2 votes
0 answers
62 views

Convergence of Truncated Newton for non-convex Hessian

I was wondering if anyone could enlighten me about the convergence properties of the truncated newton method in case of a non-positive definite hessian $\nabla^2 f = H$. From the Book 'Numerical ...
RockedSalad121's user avatar
2 votes
0 answers
203 views

Proving convexity of Frobenius norm and correlation function formulations of an optimization problem

I have been working on formulating my requirements in the form of an optimization problem in a multi-output regression setting. Firstly, I would like to make the variables I used in the problem and ...
venkat's user avatar
  • 21
1 vote
0 answers
94 views

Ramp least squares estimation

With some given $s$ value, let \begin{equation} \begin{aligned} h(\beta)&=\min(\sum_{i=1}^n(Y_i - X_i\beta)^2, s)\\ &=\sum_{i=1}^n(Y_i - X_i\beta)^2-\max(0, \sum_{i=1}^n(Y_i - X_i\beta)...
jsmath's user avatar
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1 vote
0 answers
97 views

Best optimizer for unconnstrained non-convex nonlinear least-square optimization problem?

I am looking for a very good optimizer to the following problem: $$\min_{P,\Theta}\lVert APD(\Theta)P^{-1} -B \rVert_F$$ where $A,B \in \mathbb{R}^{n\times m}$, $P \in \mathbb{R}^{m\times m}$, $D\in \...
ArtificiallyIntelligent's user avatar
6 votes
1 answer
187 views

How to solve a 4th order nonnegative LASSO problem?

I need to solve the following 4th order nonnegative LASSO problem: $$ \min_{x \geq 0} \quad || |Ax|^2 - b ||^2 + \lambda ||x||_1 $$ where $|\cdot|^2$ denotes element-wise squared. $A$ is small size (e....
WDC's user avatar
  • 163
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0 answers
1k views

Solving a non-convex optimization problem using fmincon

I am trying to solve a non-convex optimization problem using fmincon(). At each iteration, I am iteratively looking for the optimum value and when the termination ...
Susan's user avatar
  • 33
0 votes
0 answers
57 views

Minimizing the ratio of two specific non negative quadratic convex functions

$F$ is $m\times m$ diagonal, with real non negative elements $D$ is $n \times m$ complex $P$ is $n \times 1$ complex $A$ is $m \times 1$ complex. Minimize $\Gamma(A)$, with respect to $A$. $$\...
Rajesh D's user avatar
  • 141
2 votes
1 answer
149 views

Is there an optimization scheme/algorithm that converges, for this non-convex scenario but with some special properties

I have a smooth function $f(x) = \frac{g(x)}{h(x)}$ that is the ratio of two smooth convex functions $g(x)$ and $h(x)$. It is known that $f(x)$ has a global minimum, achieved at the unique point $x_0$....
Rajesh D's user avatar
  • 141
2 votes
1 answer
2k views

A robust algorithm to sort a non-convex polygon vertices

Let v_{0},...,v_{N-1} be N points in a Cartesian xy plane defining the vertices of closed polygon (i.e. v_{N} = v_{0}). Let P_{0}...
John Snow's user avatar
  • 139
1 vote
0 answers
52 views

Efficient numerical optimization of an "almost separable" function

I have come across an optimization problem with the following objective function: $$f(x_0,y_0,z_0,x_1,y_1,z_1,...,x_N,y_N,z_N) = \sum_{i=0}^N f_i(x_i,y_i,z_i, \alpha(x_{i+1}-x_i) + \beta(y_{i+1}-y_i))...
KMee's user avatar
  • 41
1 vote
1 answer
452 views

Piecewise-Linear Quadratic Optimization for an "Almost Convex" Problem

I have a 7-14 dimensional piecewise linear cost function I'd like to minimize with two quadratic terms of the form: $$ f(X) = X^tCX + d \sum_i |x_i-x^*_i|^2 + \sum_i P_i(x_i-x^0_i) $$ $$ \sum_i x_i ...
rhaskett's user avatar
  • 113
0 votes
1 answer
69 views

reduced system: primal-dual interior point method for nonconvex constrained problem

When solving a reduced KKT system of a nonlinear (and nonconvex) constrained program after eliminating slack and dual variables, how do we actually take the next step in a primal-dual method? For ...
jjjjjj's user avatar
  • 325
0 votes
1 answer
89 views

Why can't we discretize continuous domains in distributed non-convex constraint optimization problems?

Consider a non-convex distributed optimization problem. We have $X$ = a set of $n$ decision variables: $x_i$ where $i=1..n$ and $x_i \in R$, the set of Reals. We have $F$ = a set of $m$ constraint ...
rasalghul's user avatar
1 vote
0 answers
110 views

Minimizing the products of variables

My problem Maximize $$\min_{i} \{\ c_i \cdot \prod_{j \in A(i)} {x_{j}} \prod_{j \in B(i)} {y_{j}} \} $$ Subject to \begin{align} &\sum_{j \in C(k)} x_{j} = 1,\ \forall k \\ &l \leq x_{j}...
linusz's user avatar
  • 111
1 vote
0 answers
293 views

Constrained optimization: Stationary point vs. Nash point

1s question: definition of stationary point for constrained optimization As far as I know, a stationary point of a constrained optimization problem is a stationary point of the Lagrangian (that has ...
f10w's user avatar
  • 515
4 votes
1 answer
180 views

Optimization of non-smooth, non-convex, locally Lipschitz functions of type exp(-abs(x))

What would be the numerical method of choice to find minima in a non-smooth, non-convex, locally Lipschitz function $f: \mathbb{R}^n\rightarrow \mathbb{R}$. The function $f$ is mostly smooth but ...
M. Heuer's user avatar
  • 193
2 votes
0 answers
90 views

Global optimization with known distributions of some variables

I'm solving simple single-objective multidimensional global optimization problem using various stochastic algorithms like Monte-Carlo, GA and other evolutionary approaches. The task is formulated as ...
Serg's user avatar
  • 21
1 vote
0 answers
174 views

preconditioning LBFGS?

I want to minimize an energy of the form $$V_1(\mathbf{x}) + V_2(\mathbf{x})$$ where $V_1$ is much stiffer than $V_2$. When I try to use LBFGS, convergence is extremely slow, as the solution ...
user168715's user avatar
6 votes
1 answer
677 views

Global convergence in trust region algorithm

I was reading about TR methods and there are some terms, which are confusing for me. It says, method is globaly convergent. What does it really mean? Converges to global minima, or converges for ...
bla_bla_bla's user avatar
1 vote
0 answers
281 views

Numerical solution of non-linear advection equation other than inviscid burgers

I am solving a non-linear advection equation of the form $u_t + f(u)_x = 0$ where $f(u)$ is a complicated function of $u$. I am solving this equation using a first order fully implicit scheme (...
GreenEye's user avatar
25 votes
5 answers
10k views

Why should non-convexity be a problem in optimization?

I was very surprised when I started to read something about non-convex optimization in general and I saw statements like this: Many practical problems of importance are non-convex, and most non-...
Prokop Hapala's user avatar
1 vote
0 answers
153 views

Minimization of the sum of convex function and non-linear non convex function

I'm trying to minimize the unconstrained scalar sum of a quadratic convex function (to which a convex optimizer is readily applied) and a non-linear and non-convex function which is differentiable. ...
grizzlyjoker's user avatar
4 votes
2 answers
4k views

Subgradients of non-convex functions

In these notes (section 2.3), it is stated that: A point $x^*$ is a minimizer of a function $f$ (not necessarily convex) if and only if $f$ is subdifferentiable at $x^*$ and $0 \in\partial f(x^*).$ ...
f10w's user avatar
  • 515
3 votes
1 answer
275 views

Symmetric nonnegative matrix factorization

Suppose $A\in\mathbb{R}_+^{n\times n}$ is symmetric. I would like to factorize $A\approx UU^\top$ by solving $$ \begin{array}{rl} \min_U & \sum_{ij} \left(A_{ij}\ln\frac{A_{ij}}{[UU^\top]_{ij}}+[...
Justin Solomon's user avatar
2 votes
0 answers
103 views

non convex, non linear optimization involving matrix differential equation solution

I'm trying to develop an inferential procedure for a multivariate dependent Markov process. Basically, the procedure could be considered as a non linear regression, with a known dependence structure ...
user58058's user avatar
2 votes
1 answer
331 views

Minimal surface finite differences problem - Matlab assemble

I face to the following problem: $$(1+u_x^2)u_{yy} - 2u_xu_yu_{xy} + (1+u_y^2)u_{xx}=0.$$ Problem needs to be discretized and assembled. Does anybody know how to proceed in Matlab?
SmallElephant's user avatar
2 votes
0 answers
362 views

Finite difference scheme for solving nonlinear least-squares problem

I am dealing with following problem: $$ \min_{u,\gamma}\Bigg\{ \frac{1}{1000} \iint_{S_2} {\gamma (x,y)^2 dxdy} + \iint_{S_2} {[u(x,y) - u_0 (x,y)]^2 dxdy} + \iint_{S_2} {[\Delta u(x,y) - \gamma (...
SmallElephant's user avatar
4 votes
2 answers
233 views

non-convex quadratic with only one quadratic constraint?

I have a non-convex optimization problem in the form: \begin{align} \min_{b,\xi,\eta} \sum_{i=1}^{n} b_i \xi_i + \gamma \Vert \eta \Vert \cr \text{s.t.} b\geq 0, b^\mathsf{T} 1 = 1,b_i \leq \frac{1}{n-...
user85361's user avatar
  • 221
4 votes
2 answers
679 views

Finding a global minimum of non-convex quasi-smooth function that is costly to evaluate

I have a bounded non-convex function in 10-dimensional space. The function is quasi-smooth, you can imagine a histogram, here is an illustration, it just shows the idea and not related to my ...
JohnnyBGoode's user avatar
5 votes
2 answers
230 views

Is there guaranteed global solver for such an eigenvalue problem?

The original nonlinear optimization problem I have is as follows: For constant symmetric matrices $A=A^T, B_i=B_i^T(\forall i\in\mathbb{N}) \in \mathbb{R}^{n\times n}, \text{rank}(A)=n,$ $$\arg\min\...
LCFactorization's user avatar
0 votes
2 answers
150 views

Nonconvex Optimization

Consider the following optimization problem: $\text{max}_{p} \quad ||p||^2 \\ s.t: x\geq 0\\ p\in D$ where $D$ is a convex set. Is this problem $\mathcal{NP}$-hard?
Star's user avatar
  • 585
3 votes
2 answers
637 views

Affect of approximating a non-differentiable function on optimisation of minimisation

I am looking at a problem of constrained minimization, where the function to be minimized contains the Heaviside function, and as such is not twice continuously differentiable. My question is what ...
MikeW's user avatar
  • 173
0 votes
1 answer
754 views

Non convex optimization

I have the following Max Min optimization problem that appears to be non convex. link where t p c are my variables and all others are constants. I took the eigen values of the hermetian part of the ...
cody's user avatar
  • 13
6 votes
2 answers
3k views

linear independence constraint qualification: what to do when they don't hold?

I want to solve a general nonlinear constrained optimization problem $$\min_q\ f(q)\quad \textrm{s.t.}\quad g_i(q) = 0,\ h_j(q) \geq 0.$$ The problem is that while the equality constraints $g_i(q)$ ...
user168715's user avatar
2 votes
3 answers
2k views

Global optimisation with discontinuous objective

I'm looking at a global minimisation problem on the $n$-sphere ($n$ of the order 10--50) but with a complete swine of an objective --- it is (effectively) a black box; it is piecewise smooth but with ...
n00b's user avatar
  • 590
1 vote
0 answers
21 views

Reduction for NP-hardness [duplicate]

Consider the following optimization problem: \begin{align} \text{Min}_{i\neq j\neq s\neq t} |x_i x_j-x_sx_t|\\ s.t: Ax=b\\ x\geq 0; \end{align} This problem can be seen as an instance of non convex ...
Star's user avatar
  • 585
3 votes
2 answers
753 views

NP-Completeness

Consider an instance of non-convexoptimization problem: It seems that this problem is NP-complete. How can I find a suitable reduction for this?
Star's user avatar
  • 585
7 votes
1 answer
263 views

Algorithm for dealing with medium-size non-convex QCQP

I have the following problem in $x \in \mathbb C^{205}$ $$\displaystyle\min_{x}x^HAx$$ subject to the following constraints $$x^HBx = 1$$ $$x^HC_ix = 0$$ for $i \in \{0,1,\dots,203\}$, where $A$ ...
Costis's user avatar
  • 1,320