Questions tagged [convex-optimization]
The convex-optimization tag has no usage guidance.
41
questions
2
votes
0
answers
32
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
$$ \arg \min_{\boldsymbol{x}} \frac{1}{2} {\left\| A \boldsymbol{x} - \boldsymbol{y} \right\|}_2 + \lambda_1 {\...
- 616
0
votes
0
answers
21
views
Covariance fitting and Procrustes
I have seen this trick to simplify an optimization problem. I would like to understand the logic behind it.
Take two matrices $A$ and $B$ of dimension $N \times N$ and suppose that matrix $B$ is ...
- 103
1
vote
0
answers
89
views
Implementing optimization part of blind deconvolution in Python
I am trying to implement the paper Qi Shan, Zhaorong Li, Jiaya Jia, Chi-Keung Tang - Fast Image/Video Upsampling and need some help with implementing minimizing an energy function (7) shown below.
...
- 111
6
votes
1
answer
147
views
Solving a Weighted Basis Pursuit Denoising Problem (BPDN) with MATLAB / CVX
Following up from an answer by @Royi on adding weights to BPDN problem , I would like to use CVX to test this approach. How can we formulate in CVX the regularized LS L1 norm with weights given by a ...
- 616
4
votes
1
answer
81
views
Adding Variance \ Weights Information When Solving a Basis Pursuit Denoising Problem (BPDN)
Having a "measured" vector $\mathbf{y}$ with its statistics (counts or variance per element), one can use weighted least squares approach to solve the linear system $$\mathbf{A}\mathbf{x} = \...
- 616
5
votes
1
answer
87
views
How Could One Accelerate the Convergence of the Least Mean Squares (LMS) Filter?
How can the convergence of an LMS filter be accelerated?
Can we do better than the vanilla algorithm?
- 756
5
votes
1
answer
108
views
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 ...
1
vote
2
answers
112
views
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 ...
- 580
0
votes
1
answer
70
views
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 ...
- 580
5
votes
1
answer
194
views
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 ...
- 177
5
votes
2
answers
63
views
Justification for Squared $ {L}_{2} $ Data and Smoothness Term as an Error Bound
Often in variational methods (and not only) we have an energy that is of the form:
$$E(u) = \frac{1}{2}\|f-u\|^2_2 + \frac{\alpha}{2}\|\psi(u)\|^2_2,$$
where the first term is referred to as the data ...
- 205
7
votes
2
answers
329
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 ...
- 331
0
votes
1
answer
47
views
Least Squares Filter Design: Deriving the Objective Function
I'm following the derivation in this paper A Comb Filter Design Using Fractional-Sample Delay to obtain the objective function for the least-squares filter design.
N-order FIR filter:
$H(z) = \sum_{...
- 113
2
votes
0
answers
30
views
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 $
...
6
votes
1
answer
208
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 \...
- 63
0
votes
1
answer
121
views
On the Use of OMP Algorithm to Estimate Sparse Vector
As known, Orthogonal Matching Pursuit (OMP) Algorithm is to recover the sparse channel after convolution with another vector. But when I implement that in MATLAB, I don't get the sparse vector ...
- 640
5
votes
1
answer
167
views
How to solve ADMM for TV Minimization Problem For Different Sizes $A$ and $x$ in $Ax=b$
I have matrix $A$ that is $(M \times M)$ square matrix, $x$ $(M \times N)$ matrix, $b$ is $(M \times N)$ matrix. Knowing $A$ and $b$ I would like to get the $x$ from the equation $Ax=b$. $N=p \times q$...
- 75
2
votes
0
answers
22
views
Solving Sparse Model with given Dictionary Using LASSO
I was trying to solve a problem where the basis matrix contains the components of $\sin(nx)$, $\cos(nx)$, $\sinh(nx)$ and $\cosh(nx)$.
Say the $n$ varies from 1 to 100. While solving the lasso linear ...
- 191
5
votes
2
answers
300
views
Proximal Gradient Method (PGM) for a Function Model with More than 2 Functions (Sum of Functions)
I am currently working in signal reconstruction. I am trying to develop an algorithm where the user can plug any constraint to the main objective function (let's say chi2, least squares). I was trying ...
2
votes
0
answers
31
views
Rakeness Optimization problem
Rakeness optimization problem demonstrate that increases the rakeness between $a$ , $b$ while leaving $b$ random enough.
where $e$ is the energy of the projection waveforms and $r$ is a randomness-...
- 49
6
votes
1
answer
127
views
Convex Optimization with $ {L}_{1, 2} $ Regularization Term
I have an optimization problem such as follow:
$$\underset{X}{\operatorname{argmin}}\sum _s \left \| T_sX_{:,s} - Y_{:,s} \right \|^2_2 +\lambda\left \| GX \right \|_{2,1} \tag{1}$$
I have introduced ...
- 119
4
votes
1
answer
134
views
Why Does FISTA Algorithm Not Work for Signed Signals?
Using the FISTA Algorithm for compressive sensing from Tiep H. Vu - FISTA, I created the matlab example below.
I created 2 sparse signals x_signed and x_pos, where the latter only contains positive ...
- 173
5
votes
1
answer
125
views
Resources on Solving Convex Optimization Problems in the Compressed Sensing Field
When I read papers of compressed sensing, sparse representation and whatever requiring optimization of a cost function, I just find the final results as an iterative equation or so which will converge ...
- 487
5
votes
2
answers
270
views
Constrained LASSO Problem - $ {L}_{1} $ Regularized Least Squares with Linear Equality Constraints
I have an optimization question.
I want to solve the following problem:
$$
\arg\min_S\frac{1}{2}\|s-c\|_2^2 +\lambda\|\Phi s\|_1 \mbox{ s.t. } As = 0
$$
in which $\Phi$ is the wavelet transform ...
- 55
0
votes
1
answer
53
views
Wireless Body Area Networks with Minimum Energy Consumption [closed]
For adaptive compressive sensing(cs),the sensing matrix is related to the input signal.
For example, in rakeness-based(cs), the sensing matrix is obtained by solving an optimization problem which ...
- 117
1
vote
1
answer
445
views
Fast Optimization for Long FIR Filters
I need FIR filter lengths in the order of 1e4 and above to obtain reasonable accuracy in desired frequency response. The problem is that optimisation in MATLAB (e.g. fircls or the Optimization Toolbox)...
- 945
6
votes
1
answer
761
views
How to Formulate a Constraint Which Ensures All Variables Have the Same Sign
I'm trying to include a constraint in my problem (to be solved by any convex optimization solver). Let {a,b,c,d ...} be a finite set of continuous variables. How to formulate a constraint which ensure ...
- 61
5
votes
1
answer
305
views
The Gradient Operator of a Vectorized Image in Matrix Form
I have this optimization problem:
$$ \arg \min_{ X \left( i, j \right) } \sum_{i, j} \left\| X \left( i, j \right) - 255 \right\|_{2}^{2} + \lambda \sum_{i, j} \left\| \nabla X \left( i, j \right) - \...
- 115
5
votes
1
answer
712
views
Automatic Image Enhancement of Images of Scanned Documents (Auto Whitening)
Dropbox have make a blog post about there automatic enhancement method for scanned document image - Fast Document Rectification and Enhancement.
I followed the post and they mention a formula to make ...
- 115
6
votes
2
answers
2k
views
Solving LASSO ($ {L}_{1} $ Regularized Least Squares) with Gradient Descent
To the best of my knowledge, state of the art methods for optimizing the LASSO objective function include the LARS algorithm and proximal gradient methods.
I was wondering however, if the LASSO ...
- 105
0
votes
1
answer
153
views
Difference Between Iteratively Reweighted Least Squares (IRLS) and Sequential Quadratic Programming?
Part of my work is concerned with applications in Sparse Bayesian Learning and therefore I occasionally stumble over interesting papers in the field of compressed sensing.
I recently read ...
- 105
0
votes
2
answers
2k
views
Why Do Most of The Papers Use the Frobenius Norm for Denoising?
I have an noisy image and I want to remove noise from it; suppose $y$ is noisy image and $A$ is linear mask which makes my image noisy and $x$ is original image, so we have
$$
Ax + \eta = y
$$
and $\...
- 21
11
votes
1
answer
953
views
How Can I Use MATLAB to Solve a Total Variation Denoising / Deblurring Problem?
The Total Variation Denoising Problem is given by:
$$ \arg \min_{x} \frac{1}{2} {\left\| A x - y \right\|}_{2}^{2} + \lambda \operatorname{TV} \left( x \right) $$
Where $ \operatorname{TV} \left( \...
CEB12
3
votes
1
answer
3k
views
Understanding Soft Thresholding Operator
I want to understand what is soft thresholding operator? Why we use this operator? I came across this term while I was reading the paper - A New Algorithm Based on Linearized Bregman Iteration with ...
- 133
2
votes
1
answer
383
views
How Come RIP Guarantees Unique Restoration of the Sparse Solution by $ {L}_{1} $ Minimization?
We have a sensing matrix $\Phi$, satisfying the restricted isometry property (RIP), and a sparse signal $x$. We want to recover $\hat x$ from the measurement $y=\Phi x$ by using $l_1$-minimization.
I ...
- 21
2
votes
1
answer
149
views
Adaptive Filter Gradient Descent
The quadratic performance surface of an adaptive filter is a paraboloid. Its minimum can be found wherever the gradient is zero. However, since there are two types of paraboloids (elliptical and ...
- 61
6
votes
1
answer
168
views
Regularized Least Squares by Laplacian Operator - Optimal Value of the Regularization Factor (Lagrangian Multiplier)
Consider the cost function
$$f(X,\lambda) = \|AX-b\|_2^2 + \alpha \|LX\|_2^2$$
$A:$Measurement matrix($R_{m\times n}$,$m \ll n$), $b:$observation vector($R_m$), $L:$Laplacian operator($R_{n \times n}...
5
votes
2
answers
884
views
Significance of $ \lambda $ in Basis Pursuit
In basis Pursuit, L1 minimization is done to perform compressed sensing. In the literature there is a $ \lambda $ parameter used as a regularizer.
What is its significance?
- 167
9
votes
1
answer
319
views
Least Angle Regression (LARS) without Matrix Inversion
Sorry if this is too damned long. I did what I could to abbreviate it.
The question is about Least Angle Regression (LARS).
I'm new to numerical work with matrices.
I believe I have a way to ...
- 433
11
votes
1
answer
161
views
Ideas on Matrix Factorization / Transformations for $ {L}_{1} $ Minimization
I am starting with a typical $\ell_1$ basis pursuit problem:
$$
\min_{\mathbf{x}} \Vert \mathbf{x} \Vert_1 \quad \mathrm{s.t.} \quad \Vert \mathbf{ERx} - \mathbf{y} \Vert_2 \leq \epsilon,
$$
where $\...
- 739
8
votes
4
answers
802
views
Solving Convex Optimization Problem Used for High Quality Denoising
The highest voted answer to this question suggests that to denoise a signal while preserving sharp transitions one should
minimize the objective function:
$$ |x-y|^2 + b|f(y)| $$
where $...
- 1,082