قيم ومتجهات ذاتية

(تم التحويل من قيمة ذاتية)

القيمة الذاتية لنظام ما eigen value أو بالأحرى في معادلة تفاضلية هو عدد يمكن أن يكون من الأعداد الصحيحة أو من الأعداد المعقدة.

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مقاربة الرياضيات التفاضلية للقيمة الذاتية

حساب حل معادلة تفاضلية من الدرجة الأولى:


فلنعتبر مثلا المعادلة التفاضلية البسيطة التالية(مع غض الطرف مبدئيا عن وجوب إعتبار الشروط الأولى أي initial conditions عند حساب الحل):



و لنحاول البحث عن حل هذه المعادلة. المعروف هو أنه يمكن أن نقول أن حل هذالمعادلة هو:



أي أن و إذا عوضت x ب فإنك تحصل على المعادلة التالية:



أي

أي بعد أن نشطب من المعادلة فإنك تحصل على المعادلة و هذا بدوره يعني أن c=-10.

القيمة الذاتية

في عملية الحساب أعلاه وصلنا من معادلة تفاضلية من الدرجة الأولى إلى معادلة بسيطة لحساب القيمة الذاتية c ألا وهي المعادلة و يمكن تعميم هذه الطريقة أي كيفية الوصول من معادلة تفاضلية ذات درجة 2 أو 3 إلخ...إلى المعادلة لحساب القيمة الذاتية أو في هذه الحالة القيم الذاتية (لأن درجة العلاقة التفاضلية تتطابق دائما مع عدد القيم الخاصة التي تحسبها حيث يكون عليك في هذه الحالة حل بولينومات polynoms و أن تراعي طبعا أن بعض الحلول قد تكون مكررة أي أنه يجب أن تعدها عدة مرات حتى تكون ملاحظتي هذه صحيحة).
كما يجدر الإشارة إلى أن هذه الطريقة أو المعالجة حيث فقط للنظم أو المعادلات التفاضلية الخطية أي أنه في صورة إنعدام الخطية لا يمكن الحديث عن قيمة ذاتية.
كما أن القيمة الذاتية تعلمنا إذا كان نظام ما مستقرا (إذا كانت القيمة الذاتية سالبة) أو غير مستقر (إذا كانت القيمة موجبة). وهي كذلك دليل على سرعة النظام أو سرعة رده (إذا كانت القيمة المطلقة للقيمة الذاتية كبيرة فإن النظام سريع أي سرعة ردة فعله سريعة).

مقاربة الرياضيات الخطية للقيمة الذاتية

التطبيقات

القيم الذاتية للتحويلات الهندسية

The following table presents some example transformations in the plane along with their 2×2 matrices, eigenvalues, and eigenvectors.

Scaling Unequal scaling Rotation Horizontal shear Hyperbolic rotation
Illustration Equal scaling (homothety) Vertical shrink and horizontal stretch of a unit square. Rotation by 50 degrees
Horizontal shear mapping
Squeeze r=1.5.svg
Matrix



Characteristic
polynomial
Eigenvalues,


,
Algebraic mult.,



Geometric mult.,



Eigenvectors All nonzero vectors

The characteristic equation for a rotation is a quadratic equation with discriminant , which is a negative number whenever θ is not an integer multiple of 180°. Therefore, except for these special cases, the two eigenvalues are complex numbers, ; and all eigenvectors have non-real entries. Indeed, except for those special cases, a rotation changes the direction of every nonzero vector in the plane.

A linear transformation that takes a square to a rectangle of the same area (a squeeze mapping) has reciprocal eigenvalues.

معادلة شرودنگر

The wavefunctions associated with the bound states of an electron in a hydrogen atom can be seen as the eigenvectors of the hydrogen atom Hamiltonian as well as of the angular momentum operator. They are associated with eigenvalues interpreted as their energies (increasing downward: ) and angular momentum (increasing across: s, p, d, ...). The illustration shows the square of the absolute value of the wavefunctions. Brighter areas correspond to higher probability density for a position measurement. The center of each figure is the atomic nucleus, a proton.

An example of an eigenvalue equation where the transformation is represented in terms of a differential operator is the time-independent Schrödinger equation in quantum mechanics:

where , the Hamiltonian, is a second-order differential operator and , the wavefunction, is one of its eigenfunctions corresponding to the eigenvalue , interpreted as its energy.


Principal component analysis

PCA of the multivariate Gaussian distribution centered at with a standard deviation of 3 in roughly the direction and of 1 in the orthogonal direction. The vectors shown are unit eigenvectors of the (symmetric, positive-semidefinite) covariance matrix scaled by the square root of the corresponding eigenvalue. (Just as in the one-dimensional case, the square root is taken because the standard deviation is more readily visualized than the variance.


Vibration analysis

Mode shape of a tuning fork at eigenfrequency 440.09 Hz

Eigenvalue problems occur naturally in the vibration analysis of mechanical structures with many degrees of freedom. The eigenvalues are the natural frequencies (or eigenfrequencies) of vibration, and the eigenvectors are the shapes of these vibrational modes. In particular, undamped vibration is governed by

or

that is, acceleration is proportional to position (i.e., we expect to be sinusoidal in time).

In dimensions, becomes a mass matrix and a stiffness matrix. Admissible solutions are then a linear combination of solutions to the generalized eigenvalue problem

where is the eigenvalue and is the (imaginary) angular frequency. The principal vibration modes are different from the principal compliance modes, which are the eigenvectors of alone. Furthermore, damped vibration, governed by

leads to a so-called quadratic eigenvalue problem,

This can be reduced to a generalized eigenvalue problem by algebraic manipulation at the cost of solving a larger system.

The orthogonality properties of the eigenvectors allows decoupling of the differential equations so that the system can be represented as linear summation of the eigenvectors. The eigenvalue problem of complex structures is often solved using finite element analysis, but neatly generalize the solution to scalar-valued vibration problems.


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Eigenfaces

Eigenfaces as examples of eigenvectors

In image processing, processed images of faces can be seen as vectors whose components are the brightnesses of each pixel.[1] The dimension of this vector space is the number of pixels. The eigenvectors of the covariance matrix associated with a large set of normalized pictures of faces are called eigenfaces; this is an example of principal component analysis. They are very useful for expressing any face image as a linear combination of some of them. In the facial recognition branch of biometrics, eigenfaces provide a means of applying data compression to faces for identification purposes. Research related to eigen vision systems determining hand gestures has also been made.

Similar to this concept, eigenvoices represent the general direction of variability in human pronunciations of a particular utterance, such as a word in a language. Based on a linear combination of such eigenvoices, a new voice pronunciation of the word can be constructed. These concepts have been found useful in automatic speech recognition systems for speaker adaptation.


See also

Notes

References

Citations

  1. ^ Xirouhakis, A.; Votsis, G.; Delopoulus, A. (2004), Estimation of 3D motion and structure of human faces, National Technical University of Athens, http://www.image.ece.ntua.gr/papers/43.pdf 

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External links

هناك كتاب ، Linear Algebra، في معرفة الكتب.


Theory

Demonstration applets