search for: kkt1

...he tutorials : optim(c(40,0.5), fn=LogLiketot) >Error in 1 - ms : 'ms' is missing But ms is 0.5 ... So I've tried this form : optimx(c(30,50),ms=c(0.4,0.5), fn=LogLiketot) with different values for the two parameters : par fvalues method fns grs itns conv KKT1 KKT2 xtimes >2 19.27583, 25.37964 2249.698 BFGS 12 8 NULL 0 TRUE TRUE 57.5 >1 29.6787861, 0.1580298 2248.972 Nelder-Mead 51 NA NULL 0 TRUE TRUE 66.3 The first line is not possible but as I've not constrained the optimization ... but the second line would be...

...on does not, and the R-forge version still has some glitches! However, I easily ran the code you supplied by changing optim to optimx in the penultimate line. Here's the final output. KKT condition testing Number of parameters = 2 max abs gradient element = 0.004032794 test tol = 0.1018794 KKT1 result = TRUE Hessian eigenvalues: [1] 7.138974e+02 9.931285e-04 KKT2 result = TRUE KKT results: gmax= 0.004032794 evratio= 1.391136e-06 KKT1 & 2: TRUE TRUE [1] 7.138974e+02 9.931285e-04 Save results from method BFGS > zz method par fvalues fns grs hes rs conv K...

...MX, the results are that -------------------------------------------------------------------------------------------- > optimx(par=theta0, fn=obj.fy, method="BFGS", control=list(maxit=10000), > hessian=T) par fvalues method fns grs itns conv KKT1 KKT2 xtimes 1 0.6, 1.6, 0.6, 1.6, 0.7 -0.02764405 BFGS 1 1 NULL 0 TRUE NA 8.71 > -------------------------------------------------------------------------------------------- Whenever I used different initial values, the initial ones are the answer of OPTIMX(OPTIM). Would...

Hello: The development version of Ecdat on R-Forge contains a vignette in which optim(?, method=?L-BFGS-B?) stops with an error message while violating the lower bound. To see all the details, try the following: install.packages("Ecdat", repos="http://R-Forge.R-project.org") Then do "help(pac=Ecdat)" -> "User guides, package

..., I could not find anything wrong. When I remove the gradient, I've got ----------------------------------------------------------------- > optimx(par=theta0, fn=obj.fy, method="BFGS") par fvalues method fns grs itns conv KKT1 KKT2 xtimes 1 0.4423958, 0.9665069, 0.7920856, 1.1952092, 0.3083377 -0.01733672 BFGS 35 22 NULL 0 TRUE FALSE 76.02 ----------------------------------------------------------------- where the true theta is (0.5, 1.0, 0.8, 1.2, 0.6). However, I've got better results below when I tr...

...of the augmented Lagrangian. Hence, I assume (perhaps incorrectly), that auglag is automatically generating the dual problem, and attempts to find a solution to the dual problem by calling 'optim'. MY ISSUE: The code often runs successfully (converges); sometimes with satisfying (TRUE) KKT1 and KKT2, sometimes only 1 of the 2. Sometimes it fails to converge at all. When it does converge, I do not obtain the same optimum condition when I utilize different initial conditions. When it does fail to converge, I often end up with a Nan, generated when attempting to take log(f[i]), meanin...

Hello! I am trying to create an R optimization routine for a task that's currently being done using Excel (lots of tables, formulas, and Solver). However, otpim seems to be finding a local minimum. Example data, functions, and comparison with the solution found in Excel are below. I am not experienced in optimizations so thanks a lot for your advice! Dimitri ### 2 Inputs: