\$title Nonlinear Regression Problem (LEAST,SEQ=24) \$onText Nonlinear least squares estimation problem of Mitcherlisch's law. Bracken, J, and McCormick, G P, Chapter 8.4. In Selected Applications of Nonlinear Programming. John Wiley and Sons, New York, 1968, pp. 89-90. Keywords: nonlinear programming, least square estimation, nonlinear regression, econometrics \$offText Set i 'observation number' / 1*6 /; Table dat(i,*) 'basic data' y x 1 127 -5 2 151 -3 3 379 -1 4 421 5 5 460 3 6 426 1; Variable ols 'ordinary least squares' dev(i) 'deviation' b1 b2 b3; Equation dols 'definition of ols' ddev(i) 'definition of deviations' sequ 'single equation definition'; dols.. ols =e= sum(i, sqr(dev(i))); ddev(i).. dat(i,"y") =e= b1 + b2*exp(b3*dat(i,"x")) + dev(i); sequ.. ols =e= sum(i, sqr(dat(i,"y")-b1-b2*exp(b3*dat(i,"x")))); Model least 'ordinary least squares' / dols, ddev / single 'single equ definition' / sequ /; b1.l = 500; b2.l = -150; b3.lo = -5.0; b3.l = - .2; b3.up = 5.0; solve single minimizing ols using nlp; solve least minimizing ols using nlp;