Matrix Product Constraints By Projection Methods

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A Weighted Gradient Projection Method for Inverse

Jacobian matrix (EJM) method, and geometric methods for special structures, apart from the weighted least-norm (WLN) and the gradient projection method (GPM) [5] and [6]. Note that the WLN method and the GPM method are the most frequently used ones, but both methods are seriously flawed. In WLN, the

On the FOM Algorithm for the Resolution of the Linear

1.1. The Projection Method In this part, we will review generallities over iterative methods of projection for the resolution of linear systems (1), by using projections in particular subspaces, namely Krylov s s paces [1] [2]. Most actual iterative techniques used in solving large linear systems use in one way or another a projection procedure.

Mixed-Projection Conic Optimization: A New Paradigm for

We introduce symmetric projection matrices that satisfy Y 2 =Y , the matrix analog of binary variables that satisfy z2 = z, to model rank constraints. By leveraging regularization and strong duality, we prove that this modeling paradigm yields tractable convex optimization problems over the non-convex set of orthogonal projection matrices.

Fast Orthogonal Projection Based on Kronecker Product

Linear projection is one of the most widely used opera-tions, fundamental to many algorithms in computer vision. Given a vector x∈ Rd, and a projection matrix R∈ Rk×d, the linear projection computes h(x)∈ Rk: h(x)=Rx. In the area of large-scale search and retrieval in computer vision, linear projection is usually followed by quantiza-

Revisiting Frank-Wolfe: Projection-Free Sparse Convex

methods, in particular for optimization over convex hulls of an atomic set, even if those sets can only be approximated, including sparse (or structured sparse) vectors or ma-trices, low-rank matrices, permutation matri-ces, or max-norm bounded matrices. We present a new general framework for con-vex optimization over matrix factorizations,

Douglas{Rachford Feasibility Methods for Matrix Completion

By encoding each of the properties which the matrix possesses along with its known entries as constraint sets, matrix completion can be cast as a feasibility problem. That is, it is reduced to the problem of nding a point contained in the intersection of a ( nite) family of sets. Projection algorithms comprise a class of general purpose iterative meth-

Projection-like retractions on matrix manifolds

be interpreted as a projection to implicit smooth constraints. The picture is even clearer in [Ous99] which presents such a projected U-Newton for minimizing nonsmooth functions in-volving the maximal eigenvalue of symmetric matrix, using the projection onto fixed-rank matrix manifolds.

Low-order Control Design for LMI Problems Using

Alternating Projection Methods* KAROLOS M. GRIGORIADISt and ROBERT E. SKELTONS Computational techniques that exploit the geometry of the design space are proposed to solve fixed-order control design problems described in terms of linear matrix inequalities and a coupling rank constraint.


R mcorresponds to the observation, A2R n is the projection matrix, f? 2Rnis the signal of interest, and 2Rnis a vector of errors corresponding to sensor noise, quantization errors, or other measurement inaccuracies. Most methods in current literature solve the following convex 2- 1 optimization problem for estimating f?: fb arg min f2Rn

Mixed-Projection Conic Optimization

We propose to model rank constraints using a projection matrix as an additional variable The resulting framework, mixed-projection optimization, naturally extends MIO to rank constraints We show that some of the most successful tools from MIO (e.g., big -M methods, branch-and-bound,

Projection Methods for Quantum Channel Construction

Projection Methods for Quantum Channel Construction 5 over the reals in the obvious way (where the inner product is de ned as hS;Ti:= trace(ST) 2R for all S;T2Hnm). The trace inner product induces the Frobenius norm kPk:= P i;j (ReP ij) 2 + (ImP ij) 2 1=2, where ReP ijand ImP ij are the real and the complex parts of P ij, respectively.

Algorithms for Orthogonal Nonnegative Matrix Factorization

recognition model-based methods for NMF where a nonneg-ative projection matrix is estimated, instead of learning the basis matrix. Sec. IV presents our main contribution, deriving the orthogonal NMF algorithms where multiplicative updates preserve orthogonality between nonnegative basis vectors or between encoding variables. Numerical


VARIABLE ACCURACY OF MATRIX-VECTOR PRODUCTS IN PROJECTION METHODS FOR EIGENCOMPUTATION V. SIMONCINIy Abstract. We analyze the behavior of projection-type schemes, such as the Arnoldiand Lanczos methods, for the approximation of a few eigenvalues and eigenvectors of a matrix A, when A cannot be applied exactly but only with a possibly large perturbation.

Projection-based iterative methods - Uppsala University

General framework projection methods Want to solve b− Ax= 0,b,x∈ Rn,A ∈ Rn×n Instead, choose two subspaces L ⊂ Rn and K ⊂ Rn and ∗ find xe ∈ x(0) + K, such that b− Aex ⊥ L K - search space L - subspace of constraints ∗ - basic projection step The framework is known as Petrov-Galerkin conditions.

Review of Second Half of Course - Carleton University

Projection Equations Projective Space Add fourth coordinate P w = (X w,Y w,Z w, 1)T Define (u,v,w)T such that u/w =x im, v/w =y im 3x4 Matrix E ext Only extrinsic parameters World to camera 3x3 Matrix E int Only intrinsic parameters Camera to frame Simple Matrix Product! Projective Matrix M= M intM ext (Xw,Yw,Zw)T-> (xim, yim)T

Software package for mosaic-Hankel structured low-rank

2)Matrix-product constraint R= R( ) := ; where 2Rd m00; and 2Rm00 m is a full row rank matrix. The matrix-product linear constraint is a special case of the general linear constraint since vec>( ) = vec>()( I d): Note 1: Both linear constraints are supported by both

Kernel Learning with Bregman Matrix Divergences

Improve the running time from O(n3) to O(n2) per projection Allow arbitrary linear constraints, both equality and inequality For constraints Kii = 1∀i, obtain the nearest correlation matrix problem [Higham, IMA J. Numerical Analysis, 2002] Arises in financial applications New efficient methods for finding low-rank correlation matrices

ECS231 Handout Subspace Projection Methods for Solving

If W = V, then it is called an orthogonal projection method and the corresponding or-thogonality constraints in (1a) is known as the Galerkin condition. Otherwise, if W 6= V, it is called an oblique projection method and the corresponding orthogonality constraints in (1a) is known as the Petrov-Galerkin condition. 3.In matrix notation, let V

Projection methods for quantum channel construction

detect infeasibility. In the infeasible case, the alternating projection (MAP) and the Douglas Rachford (DR) projection/reflection algorithms that we use converge to the nearest points between the linear manifold defined by the linear constraints and the semidefinite cone. Infeasibility is detected if these nearest points are not equal.

Approximate Projection Methods for Decentralized

Approximate Projection Methods for Decentralized Optimization with Functional Constraints Soomin Lee, Member, IEEE, and Michael M. Zavlanos, Member, IEEE Abstract We consider distributed convex optimization prob-lems that involve a separable objective function and nontrivial functional constraints, such as Linear Matrix Inequalities (LMIs).

Mixed-Projection Conic Optimization: A New Paradigm for

, we write these constraints in matrix form, D= Diag(G)e>+ eDiag(G)> 2G, where the equality is implicitly imposed only for pairs (i;j) where d i;j is supplied. This is equivalent to: min G2Sn + Rank(G) s.t. Diag(G)e>+eDiag(G)> 2G=D: (3) Given a solution G, we can obtain the matrix of coordinates of the underlying points X(up to a

Majorization-Projection Methods for Multidimensional

Since the constraints of the nearest EDM problem are the intersection of a subspace and a convex cone, the method of alternation projection was proposed in (Glunt et al.,1990;Ga ke and Mathar,1989) with applications to the molecule conformation (Glunt et al.,1993). A Newton s method for (2.12) was developed byQi(2013).

Calibrating Least Squares Covariance Matrix Problems with

Equality and Inequality Constraints Yan Gao⁄ and Defeng Suny June 12, 2008 Abstract In many applications in finance, insurance, and reinsurance, one seeks a solution of find-ing a covariance matrix satisfying a large number of given linear equality and inequality constraints in a way that it deviates the least from a given symmetric matrix.

Epigraphical Projection and Proximal Tools for Solving

constraints. A large family of such constraints, proven to be effective in the solution of inverse problems, can be expressed as the lower level set of a sum of convex functions evaluated over different, but possibly overlapping, blocks of the signal. For this class of constraints, the associated projection operator

Factorization for Projective and Metric Reconstruction via

matrix or the corresponding rescaled measurement matrix (RMM) W M (where denotes the Hadamard product) and further factorizing W into a camera projection matrix Pb and a 3D structure matrix Xb. The projective reconstruction differs from the true reconstruction by a projective transformation. Although projective factorization

A Covariance Matrix Self-Adaptation Evolution Strategy for

variance matrix self-adaptation evolution strategy (CMSA-ES) for solving optimization problems with linear constraints. The proposed algorithm is referred to as Linear Constraint CMSA-ES (lcCMSA-ES). It uses a specially built mutation operator together with repair by projection to satisfy the constraints. The

II I1. - MIT

approximation ofthe Hessian matrix off[3], [5]. Thepurpose ofthis paperis to propose projection methods ofthe form (8) Xk+ P(Xk-akgk) where the norms and II corresponding to the projection and the differentiation operators respectively can be different. This allows the option to choose tl to match the structure ofX,therebymakingthe projection


Minimization with orthogonality constraints (e.g., X>X= I) and/or spherical constraints (e.g., kxk 2 = 1) has wide applications in polynomial optimization, combinatorial optimization, eigenvalue problems, sparse PCA, p-harmonic ows, 1-bit compressive sensing, matrix rank minimization, etc. These problems are di cult because the constraints are not

On the Generalized Essential Matrix Correction: An

matrix A, with the assumption that the latter was estimated by ignoring some of the generalized essential constraints. Our motivation for developing such a method is twofold. When, for some reason, the estimation of A does not con-sider some of the generalized essential matrix constraints or ignores them altogether (such as DLT techniques), methods


stabilization methods significantly reduces the numerical cost of simulation. if we define the matrix as a product (19) Then (16) can be written as (20) Substituting (19) in (17), we get: In the standard projection method the constraints and are stabilized simultaneously using the projection of coordinates on the manifold, given by the

Projection methods in conic optimization

memory constraints). 1.0.3 Conic projection problem The general problem that we first focus on in this chapter is the following. In the space Rn equipped with the standard inner product, we want to compute the projection of a point c ∈ Rn onto the intersection K ∩P where 2

Decomposition and Projection Methods for Distributed

each iteration consists of a parallel projection step and a consensus step. The first algorithm that we consider is equivalent to the von Neumann Alternating Projection (AP) algorithm, Bregman (1965); von Neumann (1950), in a product space E = R J1 × × R J N of dimension P N i=1 J i As a con-

Recovering Baseline and Orientation from `Essential' Matrix

Note that there must be three constraints on the nine elements of the essential matrix, since there are only six degrees of freedom (three for the baseline and three for the orientation). The essential matrix can be found given five correspondences be-tween pairs of

LNAI 5212 - A Unified View of Matrix Factorization Models

for the above factorization methods, and suggest novel generalizations of these methods such as incorporating row and column biases, and adding or relaxing clustering constraints. 1 Introduction Low-rank matrix factorization is a fundamental building block of machine learn-ing, underlying many popular regression, factor analysis, dimensionality

Sparse Projections for High-Dimensional Binary Codes

projection methods (Iterative Quantization (ITQ) [11] and Locality-Sensitive Hashing (LSH) [16]) for high-dimensional data with the same code lengths, but is also over one order of magnitude faster. Our method is more accurate than two recent methods (bilinear projections (BP) [10] and circulant binary embedding (CBE) [32]) that are

Matrix product constraints by projection methods arXiv

Matrix product constraints by projection methods Veit Elser Department of Physics Cornell University, Ithaca NY Abstract The decomposition of a matrix, as a product of factors with particular properties, is a much used tool in numerical analysis. Here we develop meth-ods for decomposing a matrix C into a product XY, where the factors X

Fast Projection-Based Methods for the Least Squares

NNMA methods into two types, namely the exact and inex-act methods. The former perform an exact minimization at each iterative step so that Ct+1 = argmin CF(Bt,C) (sim-ilarly for Bt+1), while the latter merely ensure descent, i.e. F(Bt,Ct+1) ≤ F(Bt,Ct) (similarly for Bt+1). Since the Frobenius norm of a matrix is just the sum of

Projection methods in conic optimization

comes from the presence of both (affine and conic) constraints at the same time. We will see in Section 2 that many numerical methods to compute the projection onto the intersection use combinations of projections onto P and K separately. The geometrical projection

Online adaptive passive-aggressive methods for non

Online adaptive passive-aggressive methods for non-negative matrix factorization and its applications Chenghao LIU Zhejiang University HOI, reconstruct a partially observed matrix by factorizing the matrix as the product of two low-rank matrices and imposing non-negativity


order methods are used to tackle both, small optimization problems from embedded applications and also very large optimization problems. In the case of problems with simple constraints, i.e. when it is easy to project onto the feasible set, the convergence analysis of exact projection primal first or der methods was given e.g. in [5], [17], [26].

A Simplex Algorithm - Gradient Projection Method for

W itzgall [7L commenting on the gradient projection methods of R. Frlsch and J. B. Rosen, states: More or less all algorithms for solving the linear programming problem are known to be modif-ications of an algorithm for matrix inversion. Thus the simplex method corresponds to

On Projection Matrices , and their Applications in

We also describe the multi-view constraints from these new projection matrices and methods for extracting the (non-rigid) structure and motion for each application. 1 Introduction The projective camera model, represented by the map-ping between projective spaces P 3! 2, has long been used to model the perspective projection of the pin-hole

Matrix Product State for Feature Extraction of Higher

Matrix Product State for Feature Extraction of Higher-Order Tensors savings compared to other tensor feature extraction methods such as higher-order orthogonal iteration (HOOI) underlying the value decomposition (HOSVD) when orthogonality constraints are

Camera Calibration - Carleton University

Two different calibration methods assume a set of 3d points and 2d projections Direct approach Write projection equations in terms of all the parameters That is all the unknown intrinsic and extrinsic parameters Solve for these parameters using non-linear equations Projection matrix approach

Approximate Projections for Decentralized Optimization

based methods in that, at every iteration, every agent performs a consensus update of its decision variables followed by an optimization step of its local objective function and local con-straint. Unlike other methods, the last step of our method is not a projection to the feasible set, but instead a subgradient step


the stress optimization as an Euclidean Distance Matrix (EDM) optimization with box constraints. A key element in our ap-proach is the conditional positive semidefinite cone with rank cut. Although nonconvex, this geometric object allows a fast computation of the

Numerical Methods Lecture 4: Projection Methods

OptimizationBasicsSpectral BasesDistance FunctionsAdding StatesSmolyak s AlgorithmHybrid Methods Resolving Multicollinearity Monomials seemed to lack a certain spanning property How to de ne it properly? Use a dot-product type de nition Two vectors, v 1; 2 are orthogonal i their dot-product is zero i.e. v0 1 v 2 = 0 De nition

@[email protected] Projection Methods for Quantum Channel

Projection Methods for Quantum Channel Construction Henry Wolkowicz (work with: Yuen-Lam Vris Cheung, Dmitriy Drusvyatskiy, Chi-Kwong Li, Diane Christine Pelejo) Dept. Combinatorics and Optimization University of Waterloo At: Quantum Optimization Workshop Fields