Numerische Mathematik Least Squares with a Quadratic Constraintdoi:10.1007/bf01396656Walter Gander
In this paper, we consider the problem of finding a solutionx★∈Rnthat satisfies the least-squares minimization with a quadratic inequality constraint (LSQI)minimize∥Ax−b∥2,subjectto∥Bx∥2⩽α,whereA∈Rm×n,b∈Rm,B∈Rn×n(nonsingular) andα∈R+. The norm is the standardl2-norm...
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...
Nonlinear Least Squares with Nonlinear Constraint Copy Code Copy Command Consider the following objective function, a sum of squares: 10∑k=1(2+2k+exp(kx1)+2exp(2kx22))2. The code for this objective function appears as the myfun function at the end of this example. Minimize this function...
Then J is a quadratic form in the measurement errors. When we have more confidence in the accuracy of some measurements than of others, we can choose the elements of W to weigh them more heavily than others. In this case the solution for the least squares estimate is (13.37)x^_=(HTWH...
Linear Least Squares with Nondefault Options Copy Code Copy Command This example shows how to use nondefault options for linear least squares. Set options to use the 'interior-point' algorithm and to give iterative display. Get options = optimoptions('lsqlin','Algorithm','interior-point','Dis...
points, with each iteration itself requiring the application of a least squares algorithm. Why algebraic surfaces? An algebraic presentation of a surface is an implicit presentation f(x 1 , . . . , x d ) = 0 for which f is a polynomial in its d arguments. The main rationale for our ...
We consider the problem of solving large-scale linear least squares problems that have one or more linear constraints that must be satisfied exactly. While
Adv Comput Math (2020) 46: 46 https://doi.org/10.1007/s10444-020-09749-3 Least-Squares Pade´ approximation of parametric and stochastic Helmholtz maps Francesca Bonizzoni1 · Fabio Nobile2 · Ilaria Perugia1 · Davide Pradovera2 Received: 20 July 2018 / Accepted: 29 November 2019 / ...
lsqcurvefit stopped because the final change in the sum of squares relative to its initial value is less than the value of the function tolerance. <stopping criteria details> x = 1×2 498.8309 -0.1013 Compare the solution with that of a 'levenberg-marquardt' fit. Get options = optim...