Nonlinear objectives, nonlinear constraints, non-convex objective, non-convex feasible region.

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21 views

What's the advantage of majorization-minimization (MM) algorithm

The majorization-minimization (MM) algorithm is a framework for convex and nonconvex optimization. When applied to nonconvex optimization, the MM algorithm solves a sequence of convex problems to ...
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0answers
37 views

Method of Steepest Descent - 1 iteration [on hold]

Consider the function $f : \mathbb R^2 \to \mathbb R$ given by $f(x) = x_1^2 + 4x_2^2$. I have to determine all the starting points $x = (x_1,x_2)$ on the curve $f(x)=16$ for which the method of ...
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0answers
21 views

Optimization Problem - Log-term loan [on hold]

I have to write the following problem as an optimization problem: The city of Berlin need for the construction of a tunnel in the next 6 years the following financial resources: 1996 : 20 million DM ...
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0answers
37 views

Backpropagation - error derivative [closed]

I am learning the backpropagation algorithm used to train neural networks. It kind of makes sense, but there is still one part I don't understand. I hope this is the right place to ask, here's the ...
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0answers
25 views

Nuclear norm maximization

I am trying to solve a nuclear norm maximization problem: $$\arg \max_{Q \in O(n)} \|WQV^T\|_*$$ where $Q$ is an $n \times n$ orthogonal matrix and $W$ and $V$ are real $d \times n$ matrices. I've ...
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0answers
20 views

Correct definition of submodularity

I am currently looking at a paper whose submodularity definition is different from whatever I thought I knew. In this paper, the authors consider a function $\Pi_2(q;a^r)$, where $q$ is composed of ...
-1
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2answers
81 views

Is finding a local minimizer of a NP-hard optimization problem is still NP-hard [closed]

I was wondering if for a NP-hard optimization problem, I only want to find its local minimizer, is it still NP-hard or NP-hard is only true when trying to find a global minimizer?
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0answers
12 views

Deterministic global solution to find the Optimal-knot placements for continuous piecewise linear functions to fit nonlinear data

I have been searching lately for a deterministic global technique to linearize a nonlinear function with continuous piecewise linear regions. I've a univariate nonlinear function y=f(x). where f(x) ...
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1answer
66 views

A bound on the number of bilinear functions needed in order to obtain the minmax

For $n\in\mathbb N$, let $\Delta(n)=\{x\in\mathbb R^n:x_i\geq 0, \sum_ix_i=1\}$ be the set of probability vectors in $\mathbb R^n$. Is there a function $m:\mathbb N\to\mathbb N$ such that for any ...
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0answers
29 views

Question on solving an optimization problem using Variable splitting and ADMM

Tell me if I have found the right approach to the following optimization problem: $$ min_{x} \frac{1}{2}\left \| Ax-b \right \|_2^2 \\ s.t. \ \ \Phi v=x \ , \ {x^T(1-x)}=0 $$ $A$ and $\Phi$ ...
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1answer
37 views

Envelope theorem for second derivative

I am maximizing a function $f(x,z)$ on $x$ ($z$ is treated a parameter in the maximization). The function $f$ is strictly concave on both variables. I know how to use the envelope theorem for the ...
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9 views

Binary Nonlinear Optimization Problem Transform to Continuous Form

Hi I have a mixed binary NLP problem \begin{equation} min\: f(x) \end{equation} s.t. $$ g_i(x)<=0 $$ $$ h_j(x)=0 $$ $$ x_k=1\text{ or }0 $$ I know the optimal of f(x) is approximately 0, so can I ...
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27 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. $$ g_i(x)=\frac{1}{2}x^T Q_ix-lmax_i\leq0,i\in\{1,...,m/2\} $$ $$ ...
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1answer
72 views

$0/1$ programming multiple quadratic constraints

If we have an $n$-variable rank $n$-linear system it is clear we can find whether there exists a $0/1$ solution in polynomial time. If we have an $n$-variable degree $2$ system how many constraints ...
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0answers
23 views

solution of an infinite horizon optimization problem

Give the following formulation: $\min_{\{x_s(t):\forall s,t\}} \sum_{s \in \mathcal{S}} \mathbf{1}\left(\lim_{T\rightarrow \infty} \frac{1}{T} \sum_{t=1}^T \frac{y_s(t)}{x_s(t)}\leq 1\right)$ $s.t. ...
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0answers
23 views

Finding the Lagrangian dual problem for a quadratic programm [closed]

I've problems to find the Lagrangian dual problem to \begin{align*} \min \limits_{x \in \mathbb{R}^n} \; \frac{1}{2} x^{ T} Q x + q^{T} x \\ \text{s.t.} \quad Ax &=b \\ x &\geq 0 ...
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1answer
52 views

Solving a nonlinear optimisation problem

I have the following nonlinear optimisation problem arising in my model. $$\min \sum_{k=0}^{N-1} (\tau-t_k)^+\quad \text{ s.t. } {\mathbf{x}^\top\mathbf{w}\le W,\ \mathbf{x}\ge0}, t_k=t_{k-1}+x_k ...
1
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0answers
79 views

Minimization of nonlinear integral operator

For non-negative self-adjoint traceclass operators $0\leq T \leq 1$ with $\mathrm{tr}T=N$ on the Hilbert space $L^2(\mathbb{R}^3)$ s.t. $\iint |\nabla T^\alpha(x,y)|^2 dx dy <\infty$, I would like ...
2
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84 views

Solve non-linear Optimization Problem [closed]

I have to find $x$ that minimizes: $$ \sum_{k}(x^H\textbf A_kx - b_k)^2$$ where $A_k$ are 4 x 4 positive definite matrices ($A_1, A_2,...A_k$), $x$ is 4 x 1 vector and $b_k$ are scalars ...
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169 views

Comparison of Constrained Optimization Methods

I am trying to solve a constrained optimization problem using filter methods and came across two papers on the topic that I am having some problems with. The original filter method paper is the ...
4
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0answers
70 views

max-min optimization problem

I'm curious if there is any nice way to approach solving the following kind of optimization problem. Given a $n \times m$ matrix $A = (a_{ij})$, I want to solve \begin{align*} & \max_{c}\min_{1 ...
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1answer
88 views

Convert general optimization problem to LP problem

I am trying to convert the following problem into a linear programming problem: There are $M\times N$ matrix $T$ of real numbers between 0 and 1 and $N\times 1$ vector $w$ of real numbers between 0 ...
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44 views

How to approximate higher-degree multivariate polynomial in space of lower-degree multivariate polynomials with some constraints?

For a polynomial $P_{1}(x)$, $x\in {\mathbb R}^n$ with a higher-degree, how to find a lower-degree polynomial $P_{2}(x)$ with determined structure or bounded degree to approximate it with the ...
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0answers
21 views

Maximizing modular function subject to supermodular constaint

I'm trying to solve a constrained optimization problem with submodular functions and get some nice properties of the solution. Unfortunately, I think I am in a setting where Topkis' theorem does not ...
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43 views

How to use the property of Frobenius norm in this proof?

Let $A \in \mathcal{S}^{n}$($\mathcal{S}^{n}$ is the $n \times n$ symmetric matrix space), $P,Q \in\mathbb R^{n \times n}$, and $\rho \geq 0$ be given. Show that: $$A \succeq P^{T}ZQ+Q^{T}Z^{T}P$$ for ...
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1answer
62 views

Quadrature formula for singular integrals over rectangular cuboids

A)Find the maximum of the following : $$\int_{\Omega_{a,b,c}}\int_{\Omega_{a,b,c}}\frac{dV(X)dV(Y)}{\|X-Y\|^2}$$ where ${\Omega_{a,b,c}}=[0,a]\times[0,b]\times[0,c]$, given $abc=1$ with $a,b,c>0$. ...
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99 views

Lower bound on the value $\textbf{1}^Tx$ such as $Ax\geq b$

The problem may be formulated as follows: We are given a set of $m$ positive numbers $\{b_1,...,b_m\}$ and a set of $n$ positive numbers $\{v_1,...,v_n\}$. We have $v_j\leq K$, $j=1,...,n$, for a ...
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64 views

Learning rule for recurrent neural network with flexible time steps

Summary: I want to train a recurrent network to output some answers, but the recurrent network is allowed to re-iterate through itself a flexible number of times for each input-output pair. Why this ...
2
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1answer
77 views

Better alternative to solve quadratic programming for large matrices

I have the following problem. Let's say we have $x_{jk}$ it is an expression value of gene $j$ in a sample $k$. It is the average of expression levels across the cell types $s_{ij}$, weighted by ...
3
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1answer
96 views

Computational complexity of low rank SDP

Suppose we are given a general SDP of the form with an additinal rank requirement \begin{array}{rl} {\displaystyle\min_{X \in \mathbb{S}^n}} & \langle C, X \rangle_{\mathbb{S}^n} \\ \text{subject ...
2
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76 views

Minimize L-infinity norm with restrictions

I need to minimize the following L-infinity norm with respective to $\tau$. L-infinity norm of a matrix $A$ is defined as $\|A\| = max_{i,j}|a_{i,j}|$. $$ min_{\tau} \| I -S(S+\tau)^{-1}\| $$ $$ ...
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71 views

reconstructing a linear order corrupted by noise

Suppose we have a partial order (efficiently computable), $\leq$, on $\mathbb{R}^n$, a set $S \subset \mathbb{R}^n$, and let $\rho$ be the standard Euclidean metric. We want to find a set $S^\prime = ...
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71 views

A (non-convex) minimization quadratic programming problem with d constraints

Minimize $0<\omega_{dd}<2$ subject to $$\sum_{j=1}^{d}(\omega_{dj} - \omega_{ij})^{2} \geq 4, i=0,1,...,d-1,$$ where $-2<\omega_{ij}<2$ is known for $0 \leq i \leq d-1$ and $1 \leq j \leq ...
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1answer
153 views

Bounding function of norms in constrained vector space

$v$ is a vector of length $n$, where $v_1 = 1$ and every element $v_i \in [0,1]$ $w = \| v \|_1^1 = \sum_i |v_i| = \sum_i v_i$ $x = \| v \|_2^2 = \sum_i |v_i|^2 = \sum_i v_i^2$ $y = \| v \|_3^3 = ...
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49 views

Maximizing a convex bounded function of a PSD matrix

Let $f(X)$ be convex and continuous function , with $X$ a PSD matrix. Assume that under the affine set of constraints $\mathcal{A}(X)=b$ and the convex constraint $f(X)\le1$ there is an optimal, ...
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139 views

maximize non-convex composite function

I want to maximize a composite function over a convex set \begin{equation} \begin{aligned} & \underset{\mathbf{p}}{\text{maximize}} & & f(\mathbf{p})-g(\mathbf{p})\\ & \text{subject ...
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142 views

Asymptotics to Taylor expansions?

I posted a question on MSE about approximating Taylor series but Despite a bounty I did not receive any answers or comments. Maybe you guys can help. ...
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1answer
54 views

How to compute the direction of slowest ascent from the minimum of a strongly convex function?

Consider a twice differentiable strongly convex function $f:\mathbb{R}^n \rightarrow \mathbb{R^+}$ that attains its minimum value at the point $x^*$. I am wondering if one can compute a direction of ...
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1answer
52 views

Calculating the Upper Bound on the Sphere Radius of Knotted Channel Surfaces

This question is motivated by trying to determine the upper bound on the thickness of a rope of fixed length (w.l.o.g. $2\pi$), with which a knot of given topology can be realized under the further ...
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41 views

How does one go from convexity to submodularity?

If I have a function which is convex in the hypercube, $[-1,1]^n$ then when would it imply that its restriction to $\{-1,1\}^n$ is submodular? It would be helpful is someone can share some specific ...
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2answers
102 views

convert a special case of nonlinear fractional programming into a convex problem

Is it possible to convert a fractional problem (maximization) with objective function equal to the ratio of a concave function and convex function ? This question sound impossible but I have read this ...
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2answers
131 views

Estimating the shift in the $\lambda_{max}$ of a matrix under a diagonal perturbation

Given a matrix $A$ and a diagonal matrix $D$, what ways do we have to estimate, $\lambda_{max}(A+D) - \lambda_{max}(A)$? (Feel free to make other assumptions about the matrices that they are all ...
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70 views

Is there a space in which the $\vec a$ in $\sin(a_1\cdot x)+\sin(a_2\cdot x)$ is linear?

Suppose one has equations of the form $\sin(a_1\cdot x_i)+\sin(a_2\cdot x_i)=y_i$ for $i = 1, \dots, n$ (there are also amplitudes and phase shifts, but let's ignore these for now). I want to solve ...
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62 views

Optimization question: maximize quadratic objective with semidefinite constraints

I recently encountered the following optimization problem: $\max \|AX\|_F^2$ subject to: $X\succeq0$ and $Xb_i\leq c_i$ for a collection of $T$ conditions: $i=1,\ldots,T$. Matrices $A$ and $X$ are ...
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42 views

Relaxation of non-convex QCQP with one quadratic and one linear constraint

According to Boyd we know that a non-convex QCQP problem with one quadratic constraint has strong duality with the relaxed SDP or Lagrange counterpart. (check "Convex Optimization" by Boyd, Appendix ...
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1answer
109 views

Constrained optimization (QCLP) over $x$ with the 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, given a matrix $A$ and a vector $w$, $$ \min_{x ...
5
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1answer
104 views

Resource Constrained Routing with Refueling

What are good algorithms (resp. models) for calculating optimal or near optimal routes while taking into account fuel consumption, options for refueling and, limited tank capacity? Especially modeling ...
2
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1answer
75 views

Fixed point iteration on symmetric biconvex function

Suppose $X\subseteq\mathbb{R}^n$ is a convex set and that a function $g(x,y):X\times X\rightarrow\mathbb{R}_+$ is smooth, "strictly biconvex" (strictly convex in $x$ and $y$ independently but not ...
3
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1answer
326 views

Valid KKT Constraint Qualification? Linear constraints not full rank, Jacobian of nonlinear constraints full rank and independ. of linear constraints

For a nonlinear optimization problem having only linear constraints, by the Linearity Constraint Qualification, no further constraint qualification is required for the Karush-Kuhn-Tucker (KKT) ...
3
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0answers
94 views

Determining Nullspace Basis such that only one column is deleted or added as row is added or deleted, and remaining columns of basis stay the same

I would like to compute, in MATLAB, the basis Z for the nullspace of an m by n matrix A, such that if one row of A is added (resulting in A_a), the basis for A_a is n-m-1 of the n-m columns of Z, ...