Running examples in ‘limSolve-Ex.R’ failed
The error most likely occurred in:

> ### Name: E_coli
> ### Title: An underdetermined linear inverse problem: the Escherichia Coli
> ###   Core Metabolism Model.
> ### Aliases: E_coli
> ### Keywords: datasets
> 
> ### ** Examples
> 
> # 1. parsimonious (simplest) solution
> pars <- lsei(E = E_coli$A, F = E_coli$B, G = E_coli$G, H = E_coli$H)$X
Warning in lsei(E = E_coli$A, F = E_coli$B, G = E_coli$G, H = E_coli$H) :
  LSEI error: inequalities contradictory
> 
> # 2. the optimal solution - solved with linear programming
> #    some unknowns can be negative
> 
> LP <- linp(E = E_coli$A, F = E_coli$B,G = E_coli$G, H = E_coli$H,
+            Cost = -E_coli$Maximise, ispos = FALSE)
> (Optimal <- LP$X)
        GLK1         PGI1         PFKA          FBP          FBA         TPIA 
    0.000000   807.532745   781.590686     0.000000   781.590686   781.590686 
        GAPA          PGK         GPMA          ENO         PPSA         PYKA 
 1541.434199  1541.434199  1492.089090  1492.089090     0.000000   466.657964 
        ACEE          ZWF          PGL          GND         RPIA          RPE 
 1149.295284     0.000000     0.000000     0.000000    23.623833   -23.623833 
       TKTA1        TKTA2         TALA         GLTA         ACNA         ICDA 
   -5.850762   -17.773070    -5.850762    35.435749    35.435749    35.435749 
        SUCA        SUCC1        SDHA1         FRDA         FUMA          MDH 
    0.000000     0.000000     0.000000   100.000000  -100.000000  -100.000000 
        DLD1        ADHE2         PFLA          PTA         ACKA          ACS 
    0.000000  1000.000000    10.000000     0.000000     0.000000     0.000000 
        PCKA          PPC         MAEB         SFCA         ACEA         ACEB 
    0.000000   194.384939     0.000000     0.000000     0.000000     0.000000 
         PPA         GLPK        GPSA1         RBSK         NUOA         FDOH 
    0.000000     0.000000     0.000000     0.000000   140.000000     0.000000 
        GLPD         CYOA        SDHA2        PNT1A        PNT2A         ATPA 
    0.000000    40.000000  -100.000000     0.000000   567.965512  -145.466329 
       GLCUP       GLCPTS         GLUP        RIBUP         ACUP        LACUP 
    0.000000   814.156250     0.000000     0.000000     0.000000     0.000000 
       FORUP        ETHUP       SUCCUP        PYRUP         PIUP         O2TX 
  -10.000000 -1000.000000  -100.000000   -27.796342   120.547782    20.000000 
       CO2TX         ATPM          ADK       Growth 
 -990.346093     5.870000     0.000000    33.117523 
> 
> # 3.ranges of all unknowns, including the central value and all solutions
> xr   <- xranges(E = E_coli$A, F = E_coli$B, G = E_coli$G, H = E_coli$H,
+                 central = TRUE, full = TRUE)
Warning in lsei(E = E, F = F, G = G, H = H) :
  LSEI error: inequalities contradictory
> 
> # the central point is a valid solution:
> X <- xr[ ,"central"]
> max(abs(E_coli$A%*%X - E_coli$B))
[1] 3.979039e-13
> min(E_coli$G%*%X - E_coli$H)
[1] 0
> 
> # 4. Sample solution space; the central value is a good starting point
> #   for algorithms cda and rda - but these need many iterations
> ## Not run: 
> ##D xs <- xsample(E = E_coli$A, F = E_coli$B, G = E_coli$G,H = E_coli$H,
> ##D               iter = 50000, out = 5000, type = "rda", x0 = X)$X
> ##D pairs(xs[ ,10:20], pch = ".", cex = 2, main = "sampling, using rda")
> ## End(Not run)
> 
> # using mirror algorithm takes less iterations,
> # but an iteration takes more time ; it is better to start in a corner...
> # (i.e. no need to use X as starting value)
> xs <- xsample(E = E_coli$A, F = E_coli$B, G = E_coli$G, H = E_coli$H,
+               iter = 2000, out = 500, jmp = 50, type = "mirror")$X
Warning in lsei(A = A, B = B, E = E, F = F, G = G, H = H) :
  LSEI error: inequalities contradictory
Error in xsample(E = E_coli$A, F = E_coli$B, G = E_coli$G, H = E_coli$H,  : 
  no particular solution found;incompatible constraints
Execution halted