Questions tagged [least-squares]

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Weighted Least squares with Multiple Unknowns and Iterations

I am currently working on a problem involving the minimization of the $\chi^2$ deviation between a model matrix ($C_\text{model}$) and a measured matrix ($C_\text{measured}$). by finding the best-fit ...
Elaf Salah's user avatar
1 vote
0 answers
33 views

Least square with Frobenius norm regularization

$$ \begin{align} f_i &= \operatorname*{argmin}_f \| Af - d \| ^2_{l2} + λ \| P_X(f) - L_r(P_X(f_{i-1})) \|_F^2 \\[8pt] &=M_4^{-1}(A^Hd+\lambda P_X^*(L_r(P_X(f_{i-1})))) \end{align} $$ where $$ ...
Mark Hayes's user avatar
0 votes
1 answer
136 views

Symmetric linear least-squares solution ${\bf X} {\bf A} = {\bf B}$

Given the wide matrices ${\bf A} \in {\Bbb R}^{n \times m}$ and ${\bf B} \in {\Bbb R}^{p \times m} $, where $m > n > p$, form an overdetermined linear system in ${\bf X} \in {\Bbb R}^{p \times n}...
RedOct's user avatar
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1 answer
61 views

Least square error problem ill conditioning

I am trying to understand why I am getting an almost singular matrix in a problem I have. The problem is a simple as $$ \min_{X \in \mathbb{R}^{m,n}} \left\lVert AX - B \right\rVert_F^2 $$ Obvioulsy ...
user8469759's user avatar
1 vote
1 answer
164 views

Shape, shift and scaling retrieval of a sampled function

Let $f(x)$ be some unknown continuous square-integrable function defined on the interval $[0,1]$. Suppose we have $i=1,...,n$ samples $f_i$ of $f$ of the following form: $$f_i(x)=a_i*f(x+b_i)+c_i$$ ...
dff's user avatar
  • 230
0 votes
1 answer
96 views

Trigonometry/spherical angles/minimum-least-squares [closed]

An issue from 3D tessellated geometry: Find the direction vector of a plane that minimizes the silhouette of a set of triangles. To say it another way, find the direction vector that is most ...
mattica's user avatar
  • 103
1 vote
0 answers
52 views

Least squares regression with nonnegative error

I'm looking for algorithms to solve a special quadratic programming problem, but I don't know its name or related keywords. Can anyone give me some clues? The problem reads \begin{equation} {\min}_x \...
Duo Zhang's user avatar
1 vote
3 answers
245 views

How to optimize for the best fit nonuniform-scale-rotation to a given 3×3 matrix?

Given a matrix $L\in \mathbb{R}^{3 \times 3}$, I'm looking for a method to find the closest (in a least squares sense) product of a non-uniform scaling matrix and a rotation matrix: $$ \min_{s\in\...
Alec Jacobson's user avatar
4 votes
1 answer
158 views

Least squares problem with left and right unknowns

For $i=1,...,n$, let $b_i$ be a scalar and $A_i$ be an $k\times l$ matrix. Is there a closed form solution for the following problem assuming $n>k+l$? $$\min_{x\in \mathbb{R}^k ,y\in \mathbb{R}^l} \...
dff's user avatar
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51 views

Under what conditions is the least-squares approximation bounded with the same Lipschitz gradient constants?

Let $f(x):\mathbb{R}^K\Longrightarrow \mathbb{R}^L$ denote a multivariate continuously differentiable function. All the partial derivatives of $f$ (all its Jacobian elements) are bounded from above ...
Yarden Levy's user avatar
1 vote
0 answers
142 views

Solution that minimizes the sum of squared errors, with quadratic constraints

Given symmetric and positive definite $n \times n$ (real) matrices $A_1, \dots, A_m$ and $b_1, \dots, b_m \in {\Bbb R}^{n}$, I am trying to find the solution with the least sum of squared errors of ...
gtg's user avatar
  • 11
2 votes
0 answers
121 views

Given normal equations with nonnegative solution, is there always a subsystem with nonnegative solution?

Given a system of normal equations $A^TAx=A^Tb$ where $A^TA$ is $n\times n$, what I call the $i$th subsystem is the linear system of size $n-1\times n-1$ where the $i$th column of $A$ has been removed....
Ke. Fel's user avatar
  • 29
1 vote
0 answers
25 views

Minimum eigenvalue of normal matrix with polynomial basis

For each $n\in \mathbb{N}\cup\{0\}$, let $x_n(t)=\frac{t^n}{n!}$ for all $t\in [0,1]$. As the functions $X_N=(x_0 ,\ldots, x_N)$ are linearly independent, the matrix $ \int_0^1 X_N(t)^\top X_N(t)\,\...
John's user avatar
  • 425
20 votes
4 answers
3k views

Is the pseudoinverse the same as least squares with regularization?

Given a linear system $Ax=b$, the pseudoinverse of $A$ is found as the matrix $A^+$ such that $x=A^+ b$ where $x$ solves the least squares problem $\min \| Ax - b \|^2 $ and $x \perp \mathcal{N}(A)$. ...
Herman Jaramillo's user avatar
3 votes
1 answer
272 views

Concentration inequality for norm of solution to nonlinear least-squares problem

Define the piecewise-linear function $\psi(t):=\max(t,0)$ for all $t \in \mathbb R$. Let $d,n,k \to \infty$ at the same rate (i.e $n \asymp k \asymp d$). Let $y_1,\ldots,y_n \in \{-1,1\}$ uniformly ...
dohmatob's user avatar
  • 6,466
1 vote
2 answers
168 views

Robust estimation of $Ax=b$

Problem setting : $ \underset{x}{\text{min}} \|Ax-b\|$, where $A \in \mathcal{R}^{m \times n}, m\gg n $, full rank. L1 loss is used for robust estimation using IRLS. The corresponding equation to ...
lalit's user avatar
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1 vote
1 answer
142 views

Understanding the Time Delay of Arrival trilateration algorithm

I'm trying to algorithmically solve the Time Delay of Arrival problem as part of some mathematics research. The problem is as follows: Given the location of three receivers in a plane (A, B, and C), ...
K_M's user avatar
  • 111
2 votes
2 answers
97 views

Non-parametric regression and curvature

Given a finite set of points $(x_1, y_1), (x_2, y_2), \ldots, (x_n, y_n)$ in the plane, Linear Regression tells us how to find the straight line "$y=a+bx$" best approximating the given points, in the ...
Ruy's user avatar
  • 2,183
1 vote
3 answers
84 views

RKHS/non-parametric regression with missing response values

I am interested in doing RKHS regression with missing response variables. Given input-output pairs $(x_i,y_i)$, I want to estimate a function $f(\cdot)$ as follows \begin{equation}f(x)\approx u(x)=\...
MthQ's user avatar
  • 41
2 votes
1 answer
300 views

Minimise $\sum_i \begin{Vmatrix}\boldsymbol{x}_i \\ \boldsymbol{y}_i\end{Vmatrix}$

Consider column vectors $\boldsymbol{z}_i$, $\quad i=1,\dots,n$. Each $\boldsymbol{z}_i$ has $j$ elements and can be expressed as $\boldsymbol{z}_i = \begin{bmatrix} \boldsymbol{x}_i \\ \boldsymbol{y}...
Lincoln Hannah's user avatar
5 votes
2 answers
778 views

Symmetric linear least-squares solution

Given tall matrices $A$ and $Y$ and the following overdetermined linear system in square matrix $X$ $$AX=Y$$ is there an explicit formula for the least-squares solution if $X$ is constrained to be ...
Museful's user avatar
  • 193
0 votes
1 answer
767 views

How to determine the damping factor in Levenberg-Marquardt?

From the algorithm, we can see that it tries different damping factor until it gets a good one by the error. Is the damping factor related to the eigenvalues of the Hessian matrix?
zskalibur's user avatar
2 votes
0 answers
30 views

Difference Between Total Least Squares Plane and Plane Orthogonal to Principal Axis of Inertia Tensor

Given a finite set $P$ of points in $\mathbb{E}^3$ , one can calculate an approximating plane either as the solution of a Total Least Squares problem or by interpreting the problem physically, ...
Manfred Weis's user avatar
  • 12.3k
1 vote
1 answer
112 views

Minimization Proof of Conditioning on Gaussian is Gaussian

It is well known that $E[X|X+Y]$ is Gaussian if both $X$ and $Y$ are, and the result can be derived using standard density arguments. However, how can one prove it by only resulting to optimization ...
ABIM's user avatar
  • 4,961
1 vote
0 answers
128 views

Choice of residual function for least squares error minimization

Good morning, I have the a set of data $(\sigma,D,\alpha_0)_i$, $i=1...n$ data. I want to determine two parameters $K_{IC}$, $C_f$ in the basic equation given as $K_{IC} = \sigma \sqrt{D} k_0(\...
gama's user avatar
  • 11
2 votes
2 answers
235 views

In practice, what's the fastest method to find a least square solution rather than using SVD decompostion?

I'm working on a real-time implementation of Lucas-Kanade for optical flow. However, the SVD decomposition to do achieve the least square method to reduce the error seems to take too much time. A ...
Miguel Rueda's user avatar
8 votes
3 answers
326 views

Regularized linear vs. RKHS-regression

I'm studying the difference between regularization in RKHS regression and linear regression, but I have a hard time grasping the crucial difference between the two. Given input-output pairs $(x_i,y_i)...
MthQ's user avatar
  • 41
5 votes
1 answer
268 views

Rank-constrained least-squares solution of the Sylvester matrix equation

For the Sylvester matrix equation $AX+XB=C$, I want to find the least-squares solution $X$ via $$\begin{array}{ll} \text{minimize} & \| AX + XB - C \|_{\text{F}}^2\\ \text{subject to} & \mbox{...
dave2d's user avatar
  • 191
4 votes
1 answer
347 views

Least square solution to $AXB+CXD=E$

I am trying to find the least-squares solution $X$ of the following matrix equation $$AXB+CXD=E$$ Of course, I know that this equation can be written in the form $$(B^T \otimes A+D^T \otimes C) \...
dave2d's user avatar
  • 191
3 votes
2 answers
356 views

Standard solution to semidefinite program [closed]

I have an optimization problem of the following form $$\text{minimize} \,\|Qa-b\|_2 \quad \text{ subject to } Q \succeq 0$$ where $a,b \in \mathbb{R}^n$ are given and the $n \times n$ square matrix ...
user402940's user avatar
2 votes
1 answer
178 views

Reconstruct matrix given all differences of neighbors

We have an unknown $m\times n$ matrix $X=(x_{ij})_{i=1,j=1}^{m,n}$. Assume we are given measurements of the differences $$x_{i,j+1}-x_{i,j}$$ and $$x_{i+1,j}-x_{i,j}$$ for all $(i,j)\in \{1,\...
user100927's user avatar
8 votes
1 answer
18k views

Gauss-Newton vs gradient descent vs Levenberg-Marquadt for least squared method

I need to clarify some idea I have in my mind about linear and non-linear regressions. Whatever I know about this topic comes from the book of Taylor "Introduction to error analysis": a set ...
Stefano Fedele's user avatar
0 votes
0 answers
74 views

Why is ideal wavelet selection a least-squares estimate?

In their classic paper "Ideal spatial adaptation by wavelet shrinkage" (http://biomet.oxfordjournals.org/content/81/3/425.short?rss=1&ssource=mfr), Donoho and Johnstone make the following ...
user32849's user avatar
  • 111
1 vote
1 answer
126 views

MSE of measurable function is still conditional expectation

Motivation Then the usual stochastic filtering problem says that: $$ \operatorname{argmin}_{Z \in L^2(\mathscr{G}_t)}\,\mathbb{E}[(Y_t-Z_t)^2], $$ where $\mathscr{G}_t$ is the $\sigma$-algebra ...
ABIM's user avatar
  • 4,961
1 vote
1 answer
196 views

Least squares problem with constrained solution [closed]

If $a_{m\times 1}$ and $Q_{m\times n}$ ($m<n $) are known, and we know every element of $b$ is between $[-1\ \ 1]$, how to determine $b$ to minimize $\|a+Qb\|_2$?
Peng Zhao's user avatar
2 votes
1 answer
342 views

How to force least squares solution matrix to be diagonal? [closed]

I have the following matrix equation $$AX=B$$ given $8 \times 3$ matrices $A$ and $B$. $X$ is a $3 \times 3$ diagonal matrix whose main diagonal contains the $3$ unknowns. Whenever I solve for $X$ ...
user90091's user avatar
7 votes
1 answer
201 views

Least-squares solution of systems of Sylvester equations

The Sylvester equation $AX+XB=C$ has been studied quite a lot and there are known algorithms for solving it. But has the situation where (an over-determined) system of equations $A_{i}X+XB_{i}=C_{i}$ ...
Felix Goldberg's user avatar
0 votes
2 answers
282 views

Solving sparse linear least squares or a positive definite 5-band matrix system fast

I want to quickly solve the following linear least-squares problem $$\min_{x \in \mathbb{R}^n} \left\| A x - b \right\|_2^2$$ with a special sparse structure where each row in $A$ has only up to $4$ ...
sellibitze's user avatar
3 votes
1 answer
3k views

How do I optimize over (or take derivative wrt) a square diagonal matrix?

I would like to solve the following optimization problem in $k$-vector $w_i$ $$ \min_{w_i} \quad \left\|P_i - X \mbox{diag} (w_i) Y^T \right\|_F^2 $$ where $P_i$ is a $6 \times 6$ matrix, $X$ and $Y$ ...
Jackson's user avatar
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