Least squares with a quadratic constraint - Gander - 1981 () Citation Context ...], approximation by radial basis functions [30], and in ill-posed problems [3, 4, 16, 24, 25]. These references describe several numerical methods; further solution techniques are presented by Gander =-=[7]...
1) least squares problem with constraints约束最小二乘问题2) quadratic programming problem with box constrains 框式约束最小二乘问题 1. For nonlinear l1 problembased on the conditions for optimality of the nonlinear l1 problem in [1], we first discuss the descent direction of the objective ...
Quadratically constrained least squares and quadratic problems We consider the following problem: Compute a vector x such that ∥ Axb ∥ 2 =min, subject to the constraint ∥ x ∥ 2 =α. A new approach to this problem ... GH Golub,UV Matt - Springer-Verlag New York, Inc. 被引量: 366...
Linear Least Squares: Interior-Point or Active-Set The lsqlin 'interior-point' algorithm uses the interior-point-convex quadprog Algorithm, and the lsqlin 'active-set' algorithm uses the active-set quadprog algorithm. The quadprog problem definition is to minimize a quadratic function minx12xTHx+cT...
A PROJECTION METHOD FOR LEAST SQUARES PROBLEMS WITH A QUADRATIC EQUALITY CONSTRAINT. We consider the least squares problem with a quadratic equality constraint (LSQE), i.e., minimizing ‖Ax - b‖[SUB2] subject to ‖x‖[SUB2]=α, without the... Z Zhang,Y Huang - 《Siam Journal on Mat...
Linear least squares is one of the most widely used regression methods in many fields. The simplicity of the model allows this method to be used when data
Hi , I need to solve a least squares values of the form , where x is a 32x1 vector and B is a 32x32 matrix. Howerver, x is complex and I need to constraint the solutions to make each element of vector x to have absolute value of 1. ...
We propose a technique for implementing a quadratic inequality constraint with recursive least squares (RLS) updating. A variable diagonal loading term is added at each step, where the amount of loading has a closed-form solution. Simulations under different scenarios demonstrate that this algorithm ...
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Naturally, in order to apply a least-squares (LS) solution to such models, the parameter vector a has to be somehow constrained in order to avoid the trivial solution a= 0. Usually, the problem at hand leads to a "natural" constraint on a. However, it will be shown that the use of...