Your English writing platform
Discover LudwigExact(46)
Due to the (CQ) condition, the constrained least-squares problem (13a - 13b) can be reformulated as an unconstrained least squares problem by parametrization of the s variables.
Thus, the calculation of the intensities of the virtual sources entails a nonlinear least squares problem.
First, the problem is formulated in a general setting as a standard least squares problem.
We compute approximate Legendre coefficients of the function by solving a linear least squares problem.
Then the sparsity is achieved by solving an l 1-regularized least squares problem.
When l = 1 this problem reduces to the classical least squares problem.
Similar(14)
The estimates are obtained by solving a least-squares problem.
This differential equation is numerically solved by reformulating it as a nonlinear least-squares problem.
First, a conceptual framework based on a weighted least-squares problem is developed.
We also analyze that the involved subproblems under the Frobenius norm are respectively equivalent to the structured least-squares problem and low rank least-squares problem, where the explicit solutions to some special cases are derived.
The advantage is that we need to solve a finite-dimensional least-squares problem with a few linear constraints only.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com