In this paper a modification of the standard algorithm for non-negativity-constrained linear least squares regression is proposed. The algorithm is specifically designed for use in multiway decomposition methods such as PARAFAC and N-mode principal component analysis. In those methods the typical si
Dykstra's Algorithm for a Constrained Least-squares Matrix Problem
✍ Scribed by René Escalante; Marcos Raydan
- Publisher
- John Wiley and Sons
- Year
- 1996
- Tongue
- English
- Weight
- 577 KB
- Volume
- 3
- Category
- Article
- ISSN
- 1070-5325
No coin nor oath required. For personal study only.
✦ Synopsis
We apply Dykstra's alternating projection algorithm to the constrained least-squares matrix problem that arises naturally in statistics and mathematical economics. In particular, we are concerned with the problem of finding the closest symmetric positive definite bounded and patterned matrix, in the Frobenius norm, to a given matrix. In this work, we state the problem as the minimization of a convex function over the intersection of a finite collection of closed and convex sets in the vector space of square matrices.
We present iterative schemes that exploit the geometry of the problem, and for which we establish convergence to the unique solution. Finally, we present preliminary numerical results to illustrate the performance of the proposed iterative methods.
📜 SIMILAR VOLUMES
## Abstract We consider a linear system of the form __A__~1~__x__~1~ + __A__~2~__x__~2~ + η=__b__~1~. The vector ηconsists of independent and identically distributed random variables all with mean zero. The unknowns are split into two groups __x__~1~ and __x__~2~. It is assumed that __A____A__~1~ h
## Abstract We give a polynomial‐time algorithm for finding a solution to the Traveling Salesman Problem when the points given are constrained to lie on a fixed set of smooth curves of finite length. © 2001 John Wiley & Sons, Inc.