How can I solve a problem using Constrained Nonlinear Regression?
이전 댓글 표시
Hi there,
I would like to perform contrained nonlinear regression. The scenerios for constraints are:
- Sum of parameters = n1 and each parameter < n2 (i.e. b1 + b2 + b3 = 2 and b1, b2, b3 < 1)
- Sum of parameters = n1 and each parameter ≤ n2 (i.e. b1 + b2 + b3 = 2 and b1, b2, b3 ≤ 1)
- Sum of parameters = n1 and n2 < each parameter < n3 (i.e. b1 + b2 + b3 = 2 and 0.5 < b1, b2, b3 < 1.5)
- Sum of parameters = n1 and n2 ≤ each parameter ≤ n3 (i.e. b1 + b2 + b3 = 2 and 0.5 ≤ b1, b2, b3 ≤ 1.5)
- Sum of parameters <, ≤, >, ≥ n1 or n1 <, ≤ sum of parameters <, ≤ n2 (i.e. b1 + b2 + b3 <, ≤, >, ≥ 2 or 0.5 <, ≤ b1 + b2 + b3 <, ≤ 1.5)
- Or any other alternative, if there is any remaining :)
I know that Matlab provides lsqlin for constrained linear LSQ and lsqnonline for nonlinear case. Yet, I couldn't find how to introduce summation contraint into lsqnonlin, like Aeq and beq in lsqlin.
I would be more than happy, if someone can help.
Cheers,
M
댓글 수: 1
Bruno Luong
2020년 12월 14일
No optimizer can handles strict inequalities such as < and >. Simply because it is "ill posed" minimization.
Just think about this simple example:
What is is minimum of x with the constraint x > 0?
Such probem has solution.
채택된 답변
추가 답변 (0개)
카테고리
도움말 센터 및 File Exchange에서 Linear Least Squares에 대해 자세히 알아보기
제품
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!