Questions tagged [optimization]

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Solve optimization problem?

The optimization problem is to find $n \times n$ matrix $C$ such that $\left| \left| X - D C \right| \right|_{2}^{2}$ is minimised where $X$ is $1 \times n$ and $D$ is $1 \times n$. Is this possible ...
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2 answers
50 views

FFT to work out optimum number of samples to average

I have a magnetometer, a LIS3MDL to be precise, and I am taking readings from it every second. As expected there is variation in the readings. For example, if I take five readings I get: 1164, 1190, ...
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2 votes
0 answers
31 views

constructing a weighted penalty function as function of position for elastic net

I am solving a "special" elastic net like regularized least squares problem $$ \arg \min_{\boldsymbol{x}} \frac{1}{2} {\left\| A \boldsymbol{x} - \boldsymbol{y} \right\|}_2 + \lambda_1 {\...
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Covariance fitting and Procrustes

I have seen this trick to simplify an optimization problem. I would like to understand the logic behind it. Take two matrices $A$ and $B$ of dimension $N \times N$ and suppose that matrix $B$ is ...
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3 votes
2 answers
115 views

Constrained Least Squares Filter Design

I would like to design a complex FIR filter, $h$, for a known signal that produces a desired output: $d$ = $s*h$ (where $s$ is my signal and $d$ is the desired filter output). Let $S$ be the ...
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How to interpret arg min in the the following equation?

I am studying the following equation: $$\hat{s}_m(n) = \arg \min_{s_m(n)\in A_s}\left| \frac{\psi_m^H}{||\psi_m^H||^2}y_m(n)-s_m(n)\right|^2\tag{1}$$ here $A_s$ is 1x$N$ vector of QPSK symbols, $s_m(n)...
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1 answer
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Why expected value is optimal?

The expected value is widely used and considered optimal. The question is why it is optimal. The Wikipedia page or other google search on the topic does not clearly state why and under what condition ...
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2 votes
2 answers
60 views

What procedure to find the parameters of a given filter prototype to fit a desired frequency response?

To model the frequency response of a system, I am looking for a method to fit the response of cascaded bi-quad filters to the response of that system using an optimization algorithm. The $S$-domain ...
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2 votes
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Non-rectangular meshgrid in MATLAB

I want to create a non-rectangular meshgrid in matlab. Basically I have a polygon shaped feasible set I need to make a grid of in order to interpolate 3D data points in this set. The function for ...
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3 votes
2 answers
134 views

Optimize window length (STFT) via gradient descent (in neural networks)

The authors from this paper optimized a Gaussian window size via gradient descent (the σ parameter of the bell curve) together with the other parameters of neural networks. I don't use Gaussian window ...
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Complexity of ZF precoding at the transmitter

If we have a modulated signal $s$ and ($m\times n$) MIMO channel $H$ where $m$ and $n$ are number of receive and transmit antenna, respectively. The ZF pre-coded signal is \begin{equation} x=Fs \end{...
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Is it possible to use a genetic algorithm for finding the correlation among time series

I am working on an optimized method for measuring the similarity between 2 signals, Is it possible to use a genetic algorithm for finding the correlation among time series?
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Find similarities between 2 signals

Is there any heuristic method to find metrics, similarities between two signals? I am not talking about correlation.
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Iterative beamforming IB

Is iterative beamforming (IB) an algorithm on its own, or is it an extension of the EM algorithm, how does IB differ from the latter, knowing that EM iterate 2 steps (the E step and the M step) and so ...
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How to create an objective function for Mackey glass time series (using "bayesopt")?

I am optimizing 5 hyperparameters of Mackey-Glass time series and using built-in function "bayesopt" in MATLAB. My Mackey glass time series with fixed parameters shows correct results. ...
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4 votes
1 answer
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Adding Variance \ Weights Information When Solving a Basis Pursuit Denoising Problem (BPDN)

Having a "measured" vector $\mathbf{y}$ with its statistics (counts or variance per element), one can use weighted least squares approach to solve the linear system $$\mathbf{A}\mathbf{x} = \...
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1 vote
1 answer
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Why an does an integrate-and-dump stage in a CIC filter provide the same functionality as the integrator and comb in series?

I have a hard time understanding why the combination of integrator and comb (integrate-and-dump) is the same as a separate integrator and comb in series like in this picture: Please explain why these ...
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Modern method for feature Gaussianization

Suppose non-negative feature vectors $X = [x_0, x_1, ..., x_{N-1}]$. Existing methods include: BoxCox Lambert Iterative, rotations Log-median: $$ \hat X[n, p] = \log \left(1 + \frac{X[n, p]}{C \mu[p]...
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4 votes
1 answer
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Solve Efficiently the 1D $ L_1 $ Regularized Least Squares Problem (Denoising / Deblurring)

How to solve a 1D Least Squares with $ L_1 $ Regularization? I know gradient based method, I wonder how much faster / efficient I can get. Related to Solve Efficiently the 1D Total Variation ...
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2 votes
1 answer
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Correlation and the Fourier transform

In the book Fundamentals of Music Processing: Audio, Analysis, Algorithms, Applications by Meinard Müller, the coefficients $d_\omega$ and $\phi_\omega$ are defined as where $\cos_{\omega,\phi}(t) = \...
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7 votes
1 answer
135 views

Solve Efficiently the 1D Total Variation Regularized Least Squares Problem (Denoising / Deblurring)

How to solve a 1D Least Squares with Total Variation Regularization? I know gradient based methods, I wonder how much faster / efficient I can get.
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Space-Time Finite Element and Static Condensation for Sensor Fusion

My recent pastime interest deals with the nonlinear sensor fusion of GNSS, barometer, magnetometer, accelerometer and gyroscope data. I had a look at the EKF, UKF and Particle Filters but gave up as ...
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5 votes
1 answer
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Converting Hadamard Product into Matrix Multiplication in Image Deconvolution with Total Variation (TV) Using ADMM

I would like to solve the following Image Deconvolution equation by ADMM. $$\mathbf { \min\frac{1}{2}\Vert{Cx-b}\Vert_2^2+\Vert w\circ (D x)\Vert_1 \tag 1}$$ Where, $x$ is a vector of unknown pixel ...
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5 votes
2 answers
1k views

Generate the Matrix Form of 1D Convolution Kernel

As a follow up to Generate the Matrix Form of 2D Convolution Kernel, could someone explain how to generate the matrix form of a 1D convolution kernel? How different convolutions shapes are handled? ...
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1 vote
2 answers
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Projection Matrix derivation to constrained optimization problem with Lagrange multiplier

I am trying to derive famous Projection operator as a constrained minimization problem for the least square problem. The question is as follows: Find $x$ minimizing $ (y-x)^T(y-x)$ subject to $x = H\...
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5 votes
1 answer
176 views

Tikhonov Regularization for Complex Matrices

Tikhonov regularization is used to regularize ill-posed inverse problems if the matrix $A \in \mathbb{R}^{n,m}$ to be inversed has a high condition number. For example $$ A=\begin{bmatrix}1&1\\ 1&...
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How to update point spread function of blind deconbolution by conjugate gradient?

There is an unblurred image $g$ and a blurred image $x$. Their relationship is expressed by the following formula using $psf$(point spread fucntion, size is $5×5$ kernel). $g = x \otimes psf\tag 1$ ...
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5 votes
1 answer
149 views

How to Solve the Image Dehazing Problem Using ADMM?

I want to solve the image dehazing problem using ADMM. I want to use the proximal algorithm to optimize each element. I refer to this treatise: Efficient image dehazing with boundary constraint and ...
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4 votes
1 answer
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How to Solve Blind Image Deblurring with Total Variation (TV) Prior Using ADMM?

As a continuation of the question How to Solve Non Blind Image Deblurring with Total Variation Prior Using ADMM? I would like to understand how could one solve the Blind Deblurring (Deconvolution) ...
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1 vote
2 answers
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Optimization of square matrix multiplied with another matrix to have the final result a unitary matrix

I have a square matrix $D$ whose size is $m \times m$ multiplied with another $m \times m$ square matrix $C$, I need to optimize the matrix $C$ to have a unitary matrix $DC$. I mean optimize the ...
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5 votes
1 answer
114 views

How to Solve an Image Deblurring Problem by Variational Methods Using ADMM?

Following up on a previous question, I wanted to understand how to solve an image deblurring problem using Variational methods in matlab or julia. Given some original blurry image $f$, I would like to ...
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6 votes
1 answer
159 views

How to Solve Non Blind Image Deblurring with Total Variation Prior Using ADMM?

How could one use the Total Variation frame work to solve the Deblurring problem? Specifically using the ADMM as a solver. One could assume the blurring operator is known, linear and shift invariant. ...
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  • 415
7 votes
2 answers
506 views

How to Solve Image Denoising with Total Variation Prior Using ADMM?

I was looking at some articles or Wikipedia on denoising images using the Total Variation norm. The setup is the Rudin Osher Fatemi (ROF) scheme, and the corresponding equation is: $$ F(u)=\int_{\...
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7 votes
1 answer
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Why Does the Median Filter Minimize the Absolute Value Error $L_1$ Cost Function?

I can easily prove that the mean filter minimizes the square error $L_2$ cost function using simple calculus. However, how do you prove that the median filter is optimal with respect the absolute ...
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1 answer
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time-domain channel estimation based on two vectors optimization

Let's the input data vector $X = [X_1, X_2, X_3, X_4, X_5,X_6,X_7,X_8];$ where $[X_7,X_8]$ are well known, and the vector $y = h*X$ where $*$ is the convolution operation and $h = [h_1,h_2]$ is the ...
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6 votes
1 answer
240 views

Super Resolution in Frequency Domain Using Compressed Sensing

To be noted that I'm very new to this topic, I would like to understand the fundamentals of how to get Super Resolution in Frequency Domain estimation using the Compressed Sensing Model. I am also ...
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2 votes
0 answers
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Compressed Sensing in DOA processing

I'm trying to apply the compressed sensing theory (CoSaMP algorithm) to the DOA estimation in FMCW ULA (made of 48 elements). In the dechirped signals processing I use a first FFT to solve the range ...
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5 votes
1 answer
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Is Sum of Absolute Value / $ {L}_{1} $ Norm of Differences Convex?

I'm not sure how to approach this exercise. One idea is to derive it w.r.t z, show that there is a min-extremum at $z=f_k$ and then show that for each value from the right and the left of the loss ...
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1 answer
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Transform a data set by exploting the vectorfield

I am somewhat new in the field of Digital Signal Processing / Image processing. As shown in the figure, I have 4 straight lines $f_i(x)$ with $i = 1,\dots, 4$ that pass through $g(x)$. Similiarly ...
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4 votes
1 answer
76 views

Minimize the Cost Function of Values of Vectors Based on Their Amplitude

I have two vectors $X = [x_1,x_2,x_3,x_4]$; and $Y = [y_1,y_2,y_3,y_4]$; I know that $|x_1|$ = $|y_1|$, and $|x_2|$ = $|y_2|$,... so on. it means the difference is only in the sign. it might be ...
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1 vote
1 answer
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Optimization of harmonics calculation

I need to compute the sine and cosine of an argument along with n "harmonics" \begin{matrix} \sin(x) & \cos(x) \\ \sin(2x) & \cos(2x) \\ \cdots \\ \sin(nx) & \cos(nx) \end{...
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0 votes
3 answers
132 views

Compute Hann window without cos function

In an environment with limited memory and computing power it is interesting to be able to generate a Hann window without using a cache or repetitive calling of expensive functions such as sine and ...
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1 vote
1 answer
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How to find the value of regularization parameter/s when having one or multiple priors

I am working on a image reconstruction framework, specifically interferometry. In simple words we have data that is pertubated by noise, say $$ V^o_i = V_i + \sigma_i $$ Where $V^o$ is a complex ...
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0 votes
1 answer
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How can I infer the cost function from Kruppa's simplified equations

The following equations are Kruppa's simplified equations used in camera autocalibration. My objective here is to infer the cost function(Error Function) from this equations, So I can minimize the ...
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6 votes
2 answers
237 views

Quadratic Programming with Linear Equality Constraints

I need to solve an equality constrained minimization problem as give below $$\min_{\textbf{w}} \mathbf{w}^TR\mathbf{w} $$ such that $$X\mathbf{w} = \mathbf{1}$$ where $R\in \mathbb{R}^{n\times n}$ is ...
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  • 311
0 votes
1 answer
118 views

Sparse recovery, Restricted Isometry Property for ILL-POSED problems

if $\mathbf x$ is $N\times 1$ sparse vector, and $\mathbf A$ is an $M\times N$ matrix with $M<<N$, and we measure $\mathbf y=\mathbf{Ax}$, then compressed sensing theory tells us that we can ...
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0 answers
24 views

Maximizing sum-rate with constraints

I have an SNR measure, which is a ratio of two linear functions, and I need to maximize the sum rate of a cellular system given by $$R = \sum_{i=1}^{N_{1}}\sum_{j=1}^{N_{2}}\mathrm{log}_{2}\left(1 + \...
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  • 137
2 votes
0 answers
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Image Restoration and Standard Forms of Second Order Cone Programming (SOCP)

I'm studying the application of SOCP methods in Image restoration And I want to understand the difference between the two formulas of SOCP and how they are related. Standard form (1) : min $f^{t}x $ ...
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6 votes
1 answer
189 views

Solving LASSO (Basis Pursuit Denoising Form) with LARS

I'm now working on using LARS (Least Angle Regression) algorithm to solve a LASSO problem in Basis Pursuit Denoising form like: \begin{align*} \quad && \arg \min_{\beta}{\left\| y - X\beta \...
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  • 63
5 votes
1 answer
138 views

On the Measurement Matrix Used for Compressing Sensing

Assume we have a matrix $x$ of size $(8,8)$ where each column is considered to be sparse with degree of sparsity equals to $4$. it means that every column can have $4$ zeros and $4$ non-zeros values ...
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