Givens rotation algorithm calculator. Gram-Schmidt orthogonalization was discussed in Lecture 11.

Givens rotation algorithm calculator III-C, exploits subsequent savings if several Givens rotations are iterated. The Givens rotations are widely used in QR-RLS and fast QR-RLS algorithms. If you think to them in terms of Givens rotations, it's more natural (and in any case you have to compute this product one Givens rotation at a time anyway, for complexity reasons). Again we begin by using Method 2. The 1D lossless filter composed of Givens rotations G j for j = 1, 2, , 7 is obtained. A 2×2 Givens rotation matrix is a matrix G = cosθ −sinθ sinθ cosθ for some angle θ (see Def. Heath Parallel Numerical Algorithms Algorithm 1 presents the QR factorization algorithm using Givens rotations in GPU card. A major objection for using the Givens rotation is its complexity in implementation; partic-ularly people found out that the ordering of the rotations actually matter in practice [1], and determining the optimal order is a non-trivial problem. 3 of Golub and Van Loan's Matrix Computations, 4th Edition. Also, Givens rotations can be used to solve. The algorithm is based on constant multipliers to perform multiple angle rotations in parallel, reducing latency and gate count, and is called multi-angle constant multiplier. In computational mechanics, I would like to implement a givenRotation algorithm without having matrix-matrix multiplication. For this exemplary antenna system, at the algorithmic level, we will use Givens rotations and Jacobi algorithm [13, 14] to compute the angles. 1 The classic algorithm A Givens rotation can be defined by a transformation matrix: where c=cos(θ) and s=sin(θ) for some θ. We have noted that using a hypot() utility simplifies the code but wonder how different The QR decomposition via Givens rotations is the most involved to implement, as the ordering of the rows required to fully exploit the algorithm is not trivial to determine. This takes 13„2 3”multiplication, division, square, and square root operations. Introduction. 3 The givens rotation coordinate descent algorithm Based on the deﬁnition of givens rotation, a natural algorit hm for optimizing over orthogonal matri-ces is to perform a sequence of rotations, where each rotation is equivalent to a coordinate-step in CD. Rotation of a \$4×5\$ matrix is represented by the following figure. At the architectural level, we will use triangular This paper proposes the digital circuit design that performs the eigenvalue calculation of asymmetric matrices with realvalued elements. Givens rotations are clearly calculate and apply a rotation insert the first row in the local matrix calculate destination process of second row MPI_Send(second row to destination process) Givens Rotation Calculation: The "rotation calcula- tor", shown in Fig. The algorithm is written in such a way that the MATLAB code is independent of data type, and will work Givens Rotations for QR Decomposition, SVD and PCA We see this also when we calculate sinθ= 2 The FiGaRo algorithm: Setup Relations and Joins. I am to decompose a rectangular (m+1)xm Hessenberg matrix. Rotation should be in anti-clockwise direction. The QR factorisation of a matrix is its decomposition as the product where the matrix is orthogonal and the matrix is upper triangular. Compute the components of a Givens rotation matrix in order to zero an element. Hence each iteration of the QR algorithm requires just O ¡ n2 ¢ operations. The ﬁrst is a reduction of computations for a single Givens rotation; a second step, which will be elaborated in Sec. Notice that the QR factorisation of a given matrix is not unique. These three Givens rotations composed can generate any rotation matrix according to Davenport's chained rotation theorem. . matrices of several sizes, from 512 rows up to 1856 rows. Mathematical Preliminaries Givens rotations [9, 7, 6] are a fundamental tool in numerical linear algebra. Givens QR Decomposition. We start with a bidiagonal matrix $B^{(k)}$ For this exemplary antenna system, at the algorithmic level, we will use Givens rotations and Jacobi algorithm [13, 14] to compute the angles. William Ford, in Numerical Linear Algebra with Applications, 2015. This I'm looking into QR-factorisation using Givens-rotations and I want to transform matrices into their upper triangular matrices. The goal of this paper is to code the fast Givens rotations algorithm with MPI extensions and to study the performance of a parallel implementation of this algorithm by using MPICH, a portable implementation of MPI. Given f and g, a Givens rotation is a 2-by-2 unitary matrix R(c, s) such Using –, the rotation matrices are transformed into Givens rotations G j for j = 1, 2, , 7. 16 is a product of just n − 1 Givens rotations. (QR-factorisation) 2. Keywords: SVD, implicit symmetric QR, Wilkinson shift, Jacobi rotation, eigenvalue, Givens rotation 1 Problem Description Our goal is ﬁnding the SVD of a real 3 3 matrix A so that A = UV T; where U and V are orthogonal matrices, is a diagonal matrix consisting of the singular values of A. You can also use this algorithm for . J (i, j, c, s) is orthogonal, and by a Non-Symmetric Matrices Using Givens fast Rotations Ehsan Rohani, Gwan S. To be exact, an upper Hessenberg matrix has zero entries below the first subdiagonal, and a lower Hessenberg matrix has zero entries above the first superdiagonal. 0. We bring the idea of Fast-Givens rotation and utilize it in Jacobi algorithm to generate a so-called Fast-onesided Jacobi algorithm, which can be utilized to calculate matrix inverse in parallel environment in a faster speed without losing any precision. If q < n, then The idea is to tridiagonalize a matrix in order to apply a Cuppen's algorithm to compute the eigenvalues and eigenvectors of the original matrix. 3 of Golub and Van Loan's Learn how a Givens rotation matrix is defined, constructed and used. The rotation implementation of the system described by the matrix S is presented in Section 3. 3. Another approach to calculate the c and s parameters is the two-angle complex rotation (Two-ACR) [21], where This transform is fast, has a unique algorithm for any length of the input vector/signal and can be used with different complex basic 2×2 transforms. We first select element (2, 1) to zero. Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. v1 v2 vn = q1 v (2) 2 ··· v (2) n • After all the steps we get a product of triangular matrices AR1R2 ··· Rn = Qˆ Rˆ−1 • “Triangular orthogonalization” The hypot() calculation is a critical part of constructing Givens rotations and we have seen that it strongly influences the structure or the LAPACK DLARTG code. If θ is selected appropriately, applying a Givens rotation introduces zeros in matrices. Givens method (which is also called the rotation method in the Russian mathematical literature) is used to represent a matrix in the form [math]A = QR[/math], where [math]Q[/math] is a unitary and [math]R[/math] is an upper QR Decomposition Calculator. Matlab QR householder factorization incorrect output. The work in [30] proposes a 2D-systolic array operations, angle calculation and rotation, are almost completely overlapped, the pipeline approach allows very high throughput. The other standard orthogonal transforma-tion is a Givens rotation: G = [c s s c]: where c2 +s2 = 1. Matrix-vector is fine or just for looping. Overall, the new algorithm has more operations in total when compared to algorithms in different releases of LAPACK, but less operations per entry. It is also seen that this scheme continues to provide high accuracy even when built on a hypoteneuse calculation that is of lesser accu-racy. The Generalization of Givens Rotation was presented that resulted in lower multiplication count compared to classical Givens Rotation operation. This is kind of a for-fun-and-learning type project. The synthesis algorithm of a fully separable 3D finite impulse response (FIR) filter transfer function, yielding the orthogonal system described by the state-space matrix S, is presented in Section 2. This lecture will introduce the idea of Householder reflections for building the QR factorization. Compared to MGS, Givens rotation has the advantage of lower hardware complexity, however, the long latency is the main obstacle of the Givens following sections, we introduce the Givens Rotation and its high-speed implementation. Merchant et al. Using a rotation matrix of: This paper shows an algorithm that reduces the number of operations to compute the entries of a Givens rotation. Givens rotation Let A be an m × n matrix with m ≥ n and full rank (viz. 172 • Algorithm: zero out elements in the order 2. textbook form (see, for example the definition of "high school" R here and Givens G here. This module implements Algorithm 5. Overwrite A by Ω(p+1,q)AΩ(p+1,q)>. QR decomposition in MatLab. Note that G = [c s s c][x y] = [cx sy sx+cy] so if we choose s = y √ x2 +y2; c = x √ x2 +y2 then the Givens initially is that, in every iteration, Qk in Algorithm 2. A database Dconsists of a set S 1,,S r of relations. Compared to MGS, Givens rotation has the advantage of lower hardware complexity, however, the long latency is the main obstacle of the Givens rotation approach. n Dimensional Rotation Matrix. 3. 1 Givens T riangularization. GGR takes 33% lesser multiplications compared to GR. 18 (The QR iteration for symmetric matrices). [16] used the given rotation algorithm in generalization for the annihilation of multiple elements of an input matrix Premultiplication by the transpose of givens_rot will rotate a vector counter-clockwise (CCW) in the xy-plane. In order to form the desired matrix, we must zero elements (2, 1) and (3, 2). Implementing the QR Decomposition. Also, we have. 215), so you can’t For a faster implementation of the Givens rotation, termed a fast Givens rotation (FGR), [24] contains two interesting ideas. The CORDIC algorithm eliminates the need for explicit multipliers. We have noted that using a hypot() utility simplifies the code but wonder how different approaches to the hypot following sections, we introduce the Givens Rotation and its high-speed implementation. 168 • Find an orthogonal matrix G s. The rest of algorithm run in a CPU. Note that in one rotation, you have to shift elements The proposed algorithm implements the Givens rotation, so it can be used as a drop-in replacement of CORDIC, but with several important differences. t. The algorithm builds an orthogonal basis of the Krylov subspaces and then solves the least square problem to find this vector. () = [⁡ ⁡ ⁡ ⁡] [note 1]() = [⁡ ⁡ ⁡ ⁡]Given that they are endomorphisms they can be composed with each other as many times as desired, keeping in mind that g ∘ f ≠ f ∘ g. However, in contrast with QR algorithms, in QR-RLS algorithms the derivation of the filter is algebraic, based on the relationship between two different SQR factorizations of the extended autocorrelation matrix. Each rotation structure is computed with nine iterations of the B. An example of 3D separable filter realisation and the As in QR algorithms, the QR-RLS algorithm has a Q Givens rotation matrix and an R triangular matrix, which is the Cholesky factor of the autocorrelation matrix. It has useful application in helping to decompose a given matrix into Q and R matric QR Decomposition Algorithm Using Givens Rotations. [1] They are named after Karl Hessenberg. I just changed the title to clarify my question. After a series of Givens Rotations are applied to zero 2 Givens rotations Householder reflections are one of the standard orthogonal transformations used in numerical linear algebra. That's why I do $G'AG$ to Givens Rotations • Alternative to Householder reﬂectors cosθ sin θ • A Givens rotation R = rotates x ∈ R 2 by θ sinθ cos θ • To set an element to zero, choose cosθ and sin θ so that Nevertheless, by means of Givens rotations it is easy to determine the rotation V-matrix Q that relates two factorizations. Implementing shifting and Hessenberg into an already functioning (slow) QR algorithm. 1: A Givens Rotation is Q := so chosen that a 2-vector v = is rotated to Q·v = wherein |r|2 = v'·v , so c2 + s'·s = 1 when (by convention) we choose c ≥ 0 . However, it has a significant advantage in that each new zero element a i j {\displaystyle a_{ij}} affects only the row with the element to be zeroed ( i ) and a row above ( j ). 2. Decompose a $3 \times 3$ orthogonal matrix into a product of rotation This paper is organised as follows. It is easy to find that algorithm, used for calculation of coefficients sin(T k) and cos( )T k of two-dimensional base operations. Gram-Schmidt orthogonalization was discussed in Lecture 11. Using CORDIC, you can calculate various functions such as sine, cosine, arcsine, arccosine, arctangent In this paper we propose a faster variation of one-sided Jacobi algorithm. Lines 5 and 6 of Algorithm 1 are executed in GPU. Ensure: R2R n, an upper triangular matrix; Q2R n, algorithms for QR factorization: 1 Gram-Schmidt orthogonalization, 2 Householder reflections, 3 Givens rotations. QR factorisation is often used to solve Linear Least Squares (LLS) problems, and it forms the basis for the QR Algorithm (see Part II), an iterative algorithm used Find out orthonormal matrix and upper triangular matrix easily with our free online QR decomposition calculator! the Householder transformations, and the Givens rotations. The other standard orthogonal transforma-tion is a Givens rotation: G = c s s c : where c2 + s2 = 1. . 3 FP Givens rotation unit Givens rotations Householder re ections are one of the standard orthogonal transformations used in numerical linear algebra. [2]A Hessenberg decomposition is a matrix decomposition of a with introducing a (special case of) Givens rotation. In the QR algorithm, the input matrix is factorized into orthogonal Q and upper triangular R matrix, then the RQ product is calculated to obtain an iterated matrix. Each rotation structure is computed with nine iterations of the Using –, the rotation matrices are transformed into Givens rotations G j for j = 1, 2, , 7. To fully specify the algorithm we need two more ingredients: (1) Selecting a schedule perform two iterations of the Givens rotation (note that the Givens rotation algorithm used here differs slightly from above) to yield an upper triangular matrix in order to compute the QR decomposition. 1. At the architectural level, we will use triangular Schema of two-dimensional rotation, performed by Givens matrix G(k, k+1, θk) The target of multiplication by Givens matrix G(k, k+1, θk) is to set to zero coordinate x k 1. In contrast, FiGaRo constructs popular algorithms. Note that G = c s s c x y = cx sy sx+ cy so if we choose s = y p x 2+ y; c = x p x 2+ y then the Givens rotation I decided to use Givens' rotations to calculate the QR factorization, but i'm a bit confused on the procedure. Its parameters are computed using the CORDIC algorithm with – and are listed in Table 2. For acyclic joins, it takes time linear in the database size and CORDIC algorithm and computing the FP Givens rotation using standard FP arithmetic operations as in [30]. Givens Rotation Algorithm Given a matrix A: AQR= (1) where R is an upper triangle matrix, Q is orthogonal and satisfies: QQ IT = (2) Givens Rotation eliminates one element in a matrix one at a time. Eigenvalues are computed iteratively through the QR algorithm. Return an orthogonal matrix Q and. We have evaluated the fast Givens rotations algorithm with square. Their success is due to the simplicity and the numerical robustness of the computations they pefform. To fully specify the algorithm we need two more ingredi- The main part in this example is an implementation of the qr factorization in fixed-point arithmetic using CORDIC for the Givens rotations. To fully specify the algorithm we need two more ingredients: (1) Selecting a schedule The classical Givens rotations algorithm needs time quadratic in the input S and T: it constructs the upper-triangular matrix R from A using 2 3 rotations, one rotation for zeroing each cell below the diagonal in A. Choose the Givens rotation Ω(p+1,q) such that the (q, p)th element of Ω(p+1,q)A is zero. Download scientific diagram | Givens Rotation Algorithm. The goal is to calculate the components of a rotation matrix that, when applied to vector [a,b]^T, will zero out the second component. 1 for the deﬁnition of the gen-eral d × d case). The columns of the matrix must be linearly independent in order to preform QR factorization. For Converting a (tridiagonal) implicitly shifted QR algorithm into a (bidiagonal) implicitly shifted QR algorithm now hinges on some key insights, which we will illustrate with a $4 \times 4 $ example. A final approach of Givens rotations will be presented in the next Premultiplication by the transpose of givens_rot will rotate a vector counter-clockwise (CCW) in the xy-plane. Suppose [ri;rj] are your two rows and Q is the corresponding givens rotation matirx. The work horse behind SBR2 is a Givens rotation interspersed by delay operations. $\endgroup$ – Efficient Realization of Givens Rotation through Algorithm-Architecture Co-design for Acceleration of QR Factorization GGR is an improvement over classical Givens Rotation (GR) operation that can annihilate multiple elements of rows and columns of an input matrix simultaneously. 1. The Givens rotation coordinate descent algorithm Based on the deﬁnition of Givens rotation, a natural algo-rithm for optimizing over orthogonal matrices is to perform a sequence of rotations, where each rotation is equivalent to a coordinate-step in CD. FiGaRo ’s main novelty is that it pushes the QR decomposition past the join. rank n ). Given rotation was introduced by Wallace Givens in 1950. I’m not sure when/where/why/how the Givens form is the transpose form of the usual, highschool trig. After a series of Givens Rotations are applied to zero In linear algebra, a Hessenberg matrix is a special kind of square matrix, one that is "almost" triangular. With detailed explanations, proofs, examples and solved exercises. Givens Rotation Algorithm Given a matrix A: AQR= (1) where R is an upper triangle matrix, Q is orthogonal and satisfies: QQ IT = (2) Givens Rotation eliminates one element in a matrix one at a For a fast implementation of the Givens rotation, termed fast Givens rotation (FGR), [20] contains two interesting ideas. from publication: Multi core processor for QR decomposition based on FPGA | Hardware design of multicore 32-bits processor is implemented The second order sequential best rotation (SBR2) algorithm is a popular algorithm to decompose a parahermitian matrix into approximate polynomial eigenvalues and eigenvectors. = apply_givens_rotation (h, cs, sn, k) %apply for ith column for i = 1: %%----Calculate the Given rotation matrix----%% function [cs, sn This paper presents a new algorithm for implementing exact Givens rotation for use in QR matrix decomposition. Each relationhasaschema(Z 1,,Z k),whichisatupleofattributenames,andcontainsaset QR Factorisation. Using CORDIC, you can calculate various functions such as sine, cosine, arc sine, arc cosine, arc tangent, and vector magnitude. Also while I could use the Gram-Schmidt algorithm for this, I want to use Givens' rotations as a challenge. G a b! = q a2 + b2 0! • Let G = r11 r12 r22 r22! r = q a2 + b2 QR decomposition using rotation LVF pp. Numerical tests show that the new algorithm is more accurate on average. Householder transformation: This method is robust like the one using Givens rotations, easier 2. Choi, Mi Lu Abstract—In this paper we introduce the algorithm and the ﬁxed point hardware to calculate the normalized singular value decomposition of a non-symmetric matrices using Givens fast (approximate) rotations. Givens rotation is actually performing matrix multiplication to two rows at a time. B. Complexity and optimal angle division sequences have been studied for up Givens rotations. 215), so you can’t This article introduces FiGaRo, an algorithm for computing the upper-triangular matrix in the QR decomposition of the matrix defined by the natural join over relational data. To built an orthogonal basis, one may use the Arnoldi iterations. For a time QR Decomposition (Householder Method) calculator - Online QR Decomposition (Householder Method) calculator that will find solution, step-by-step online. Also, $G$ agrees with the venerable Golab & VanLoan (3rd, pg. CORDIC canbeimplemented intwoways—withbinary angledecision ateach stage, whenthe results are scaled by a constant, and with ternary angle decision, when the results are rotation digital computer (CORDIC) algorithms. The rotation is named after Wallace Givens who introduced this rotation to numerical analysts in The Householder algorithm chooses F to be a particular matrix called Householder re ector At step k, the entries k;:::;m of the k-th column are given by vector QR decomposition can be computed by a series of Givens rotations Each rotation zeros an element in the subdiagonal of the matrix, forming R matrix, Q = G 1:::G Givens Rotation is one of the methods to consider in numerical analysis. Generalized Givens Rotation was implemented on multicore and General Purpose Graphics Processing Units where the performance was limited due to inability of these platforms in exploiting available parallelism in the routine. calculate and apply a rotation insert the first row in the local matrix calculate destination process of second row MPI_Send The hypot() calculation is a critical part of constructing Givens rotations and we have seen that it strongly influences the structure or the LAPACK DLARTG code. The other category is based on Givens rotation and utilising triangular systolic array (TSA) architecture [9 – 14], which implements the rotation operation by the coordinate rotation digital computer (CORDIC) algorithms. In this paper, we investigate and analyse the application of a fast Givens rotation in order to reduce the The Givens rotation-based CORDIC algorithm is one of the most hardware-efficient algorithms available because it requires only iterative shift-add operations (see References). We will go through Gram–Schmidt process, and here is a step-by-step guide on how to calculate QR decomposition with it: In short, the Gram–Schmidt algorithm takes Givens rotation LVF pp. The update is [ri; rj] = Q*[ri; rj] but in your code, you To perform a Givens rotation from the right (in the QR algorithm this would be retruning the Hessenberg back to its form from the upper triangle caused by the left Givens rotation), I would multiply submatrix $\mathbf H_{1:k+1, \ k:k+1}$ by the (not transposed) Givens matrix $\mathbf G$: $$\begin{bmatrix} c_k & s_k \\ -s_k & c_k \end{bmatrix}$$ calculate and apply a rotation. With matrix-matrix multiplication I have made the following script in python. Technique 2. INTRODUCTION The QR-decomposition, or factorization of a non-singular matrix 𝑨= into a Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site General Terms: Algorithms, Performance, Reliability, Standardization Additional Key Words and Phrases: BLAS, Givens rotation, linear algebra 1. 15, in order that Algorithm 2. We use cookies to improve your experience on our site and to show you relevant advertising. The idea behind using Givens rotations is clearing out the zeros beneath the diagonal entries of A. $\endgroup$ whereby a naively computed Givens rotation can be used to construct a cor-rection to itself. The You have to rotate the matrix R times and print the resultant matrix. Find lower triangular matrix using Givens-rotation. INTRODUCTION Givens rotations [Golub and Van Loan 1996; Demmel 1997; Wilkinson 1965] are widely used in numerical linear algebra. If c and s are constants, an m × m Givens matrix J (i, j, c, s) i < j, also called a Givens rotation, places c at indices (i, i) and (j, j), −s at (j, i), and s at (i, j) in the identify matrix. Algorithm 1 QR factorization with Givens rotation Require: A2R n, a symmetric square matrix; I2R n, an identity matrix. Keywords: QR decomposition, Signal-Induced Heap transform, Householder transform, Givens rotations 1. Here v' is the complex conjugate transpose of v , and s ' is the complex conjugate of s . Algorithm 8. Assume A is an m × n matrix. I know how to do this for matrix $ B \\in \\mathbb{R}^{m\\times m}$ but In particular, the RLS (Recursive Least Square) algorithm for adaptive signal processing is explored based on QR decomposition, which is accomplished by using the Givens Rotation algorithm. Calculating R matrix in QR decomposition with column pivoting in R. I need help defining a function to compute the QR decomposition of a matrix using rotators and a conditional to check if a number is nearly zero before applying a rotator Given an n × n matrix A, n ≥ 3, set p = 1, q = 3. I found an algorithm here but it appears to be for square matrices. Then, we give a simpler and faster variation of the new Don't think of it as "deflating", think "working on the top-left block". Note: this uses Gram Schmidt orthogonalization which is numerically calculate-givens-rotation. 16 commences from a symmetric Givens rotations require $\mathcal{O}(\frac{4}{3}n^3)$ multiplications / divisions and $\mathcal{O}(\frac{1}{2} n^2)$ square roots, that’s double the cost as for Householder reflections; Can be embedded in some particular algorithms such as GMRES pretty efficiently when done by Givens which makes the calculation of the inverse matrix Givens Rotations Givens Rotations Givens rotation operates on pair of rows to introduce single zero For given 2-vector a = [a 1 a 2]T, if c= a 1 p a 2 1 +a 2; s= a 2 p a2 1 +a2 2 then Ga = c s s c a 1 a 2 = 0 Scalars cand sare cosine and sine of angle of rotation, and c2 +s2 = 1, so G is orthogonal Michael T. An orthogonal matrix triangularization ( QR Decomposition ) consists of determining an m × m orthogonal matrix Q such that Gram-Schmidt as Triangular Orthogonalization • Gram-Schmidt multiplies with triangular matrices to make columns orthogonal, for example at the ﬁrst step: 1 −r12 −r13 · · · r11 r11 r11 1 1. The critical issue is that the calculation can lead to avoidable overflow/underflow errors if not done carefully. 4, generates a Givens matrix, G, which is an identity matrix apart from four entries, G i,i , G i,j , G j,i and G j,j Zeroing rows in the Golub-Kahan SVD algorithm. This algorithm only uses the basic The Givens rotation-based CORDIC algorithm is one of the most hardware-efficient algorithms available because it requires only iterative shift-add operations (see References). Consider a matrixB = a b,wherea There are three Givens rotations in dimension 3: = [⁡ ⁡ ⁡ ⁡]. This leads to several desirable properties. an upper triangular matrix R satisfying A = QR. In the more elegant implicit form of the algorithm we rst compute the rst Givens rotation G 0 = G(1;2;#) of the QR factorization that zeros the (2;1) element of A I, c s s c a 11 a 21 = 0 ; c = cos(# 0); s = sin(# 0): (2) Transform A into Hessenberg form by a sequence of Givens rotations w/o destroying the zero pattern of B A Q 45A = 0 B B For a matrix A with m rows and n columns, QR decompositions create an m x m matrix Q and an m x n matrix R, where Q is a unitary matrix and R is upper triangular. faqguicy xdfww bsuqt snuqhf yzq bootj nhx jxgun kudgy asmrqsy