List of Russian physicists: Difference between revisions

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Also visit my web site ... hostgator1centcoupon.info In mathematics and machine learning, basis pursuit denoising (BPDN) is one approach to solving a mathematical optimization problem of the form:

\min_{x} \frac{1}{2} ‖ y - A x ‖_{2}^{2} + λ ‖ x ‖_{1} .

where $λ$ is a parameter that controls the trade-off between sparsity and reconstruction fidelity, $x$ is an $N \times 1$ solution vector, $y$ is an $M \times 1$ vector of observations, $A$ is an $M \times N$ transform matrix and $M < N$ . This is an instance of convex optimization and also of quadratic programming.

Some authors refer to basis pursuit denoising as the following closely related problem:

\min_{x} ‖ x ‖_{1} subject to ‖ y - A x ‖_{2}^{2} \leq δ

which, for any given $λ$ , is equivalent to the unconstrained formulation for some (usually unknown a priori) value of $δ$ . The two problems are quite similar, but most specialized numerical algorithms can only solve the unconstrained formulation.

Either type of basis pursuit denoising solves a regularization problem with a trade-off between having a small residual (making $y$ close to $A x$ in terms of an $L_{2}$ distance) and making $x$ simple in the $L_{1}$ sense. It can be thought of as a mathematical statement of Occam's razor, finding the simplest possible explanation ( $\min_{x} ‖ x ‖_{1}$ ) capable of accounting for the observations ( $\min_{x} ‖ y - A x ‖_{2}^{2}$ ).

Exact solutions to basis pursuit denoising are often the best computationally tractable approximation of an underdetermined system of equations.Template:Cn Basis pursuit denoising has potential applications in statistics (c.f. the LASSO method of regularization), image compression and compressed sensing.

As $λ \to 0$ (or when $δ = 0$ ), this problem becomes basis pursuit.

Solving basis pursuit denoising

The problem is a convex quadratic problem, so it can be solved by many general solvers, such as interior point methods. For very large problems, many specialized methods that are faster than interior point methods have been proposed.

Several popular methods for solving basis pursuit denoising include the in-crowd algorithm (a fast solver for large, sparse problems^[1]), homotopy continuation, fixed-point continuation (a special case of the forward backward algorithm) and spectral projected gradient for L1 minimization (which actually solves LASSO, a related problem).

References

43 year old Petroleum Engineer Harry from Deep River, usually spends time with hobbies and interests like renting movies, property developers in singapore new condominium and vehicle racing. Constantly enjoys going to destinations like Camino Real de Tierra Adentro.

External links

A list of BPDN solvers at the sparse- and low-rank approximation wiki.

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↑ See The In-Crowd Algorithm for Fast Basis Pursuit Denoising, IEEE Trans Sig Proc 59 (10), Oct 1 2011, pp. 4595 - 4605, [1], demo MATLAB code available [2]

[1] See The In-Crowd Algorithm for Fast Basis Pursuit Denoising, IEEE Trans Sig Proc 59 (10), Oct 1 2011, pp. 4595 - 4605, [1], demo MATLAB code available [2]

[1]

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+{{technical|date=November 2013}}
+In [[mathematics]] and [[machine learning]], '''basis pursuit denoising''' (BPDN) is one approach to solving a [[mathematical optimization]] problem of the form:
+:<math>\min_x \frac{1}{2}\|y-Ax\|^2_2+\lambda\|x\|_1.</math>
+where <math>\lambda</math> is a parameter that controls the trade-off between [[Sparse_vector|sparsity]] and reconstruction fidelity, <math>x</math> is an <math>N \times 1</math> solution vector, <math>y</math> is an <math>M \times 1</math> vector of observations, <math>A</math> is an <math>M \times N</math> transform matrix and <math>M < N</math>. This is an instance of [[convex optimization]] and also of [[quadratic programming]].
+Some authors refer to basis pursuit denoising as the following closely related problem:
+:<math>\min_x \|x\|_1 \;\textrm{subject} \ \textrm{to}\;\;\|y-Ax\|^2_2 \le \delta</math>
+which, for any given <math>\lambda</math>, is equivalent to the unconstrained formulation for some (usually unknown ''a priori'') value of <math>\delta</math>. The two problems are quite similar, but most specialized [[numerical algorithm]]s can only solve the unconstrained formulation.
+Either type of basis pursuit denoising solves a [[Regularization (mathematics)|regularization]] problem with a trade-off between having a small residual (making <math>y</math> close to <math>Ax</math> in terms of an <math>L_2</math> distance) and making <math>x</math> simple in the <math>L_1</math> sense. It can be thought of as a mathematical statement of [[Occam's razor]], finding the simplest possible explanation (<math>\min_x \|x\|_1</math>) capable of accounting for the observations (<math> \min_x \|y-Ax\|^2_2</math>).
+Exact solutions to basis pursuit denoising are often the best computationally tractable approximation of an underdetermined system of equations.{{cn|date=January 2014}}  Basis pursuit denoising has potential applications in statistics (c.f. the [[Lasso_(statistics)#Lasso_method|LASSO]] method of [[Regularization (mathematics)|regularization]]), [[image compression]] and [[compressed sensing]].
+As <math>\lambda \rightarrow 0</math> (or when <math>\delta = 0</math>), this problem becomes [[basis pursuit]].
+==Solving basis pursuit denoising==
+The problem is a convex quadratic problem, so it can be solved by many general solvers, such as [[interior point method]]s. For very large problems, many specialized methods that are faster than interior point methods have been proposed.
+Several popular methods for solving basis pursuit denoising include the [[in-crowd algorithm]] (a fast solver for large, sparse problems<ref>See ''The In-Crowd Algorithm for Fast Basis Pursuit Denoising'', IEEE Trans Sig Proc 59 (10), Oct 1 2011, pp. 4595 - 4605, [http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5940245], demo [[MATLAB]] code available [http://molnargroup.ece.cornell.edu/files/InCrowdBeta1.zip]</ref>), [[homotopy continuation]], [[fixed-point continuation]] (a special case of the [http://www.ugcs.caltech.edu/~srbecker/wiki/Forward_Backward_Algorithm forward backward algorithm]) and [[spectral projected gradient for L1 minimization]] (which actually solves [[Lasso_(statistics)#Lasso_method|LASSO]], a related problem).
+==References==
+{{reflist}}
+==External links==
+*A list of [http://ugcs.caltech.edu/~srbecker/wiki/Category:Solvers BPDN solvers] at the [http://ugcs.caltech.edu/~srbecker/wiki/Main_Page sparse- and low-rank approximation wiki].
+[[Category:Mathematical optimization]]
+{{Mathapplied-stub}}

List of Russian physicists: Difference between revisions

Revision as of 22:11, 14 January 2014

Solving basis pursuit denoising

References

External links

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