Thanks a lot for your help, xypron and Andrew. I think the problem can be solved by adding a term like |x-x_0| in my objective function where x_0 is my initial solution, as you suggested.
I looked at glp_set_row_stat and glp_set_col_stat, but I do not understand how they can be used to set initial values (say, for columns)? In these functions, I can only set the status for rows/columns to be basic (GLP_BS) or non-basic (GLP_NL). Thanks. --Jinwei On Thu, Feb 21, 2008 at 4:13 PM, Andrew Makhorin <[EMAIL PROTECTED]> wrote: > > I am wondering how we can do initialization in GLPK. > > > In my problems, I am optimizing several thousands of small LP > > problems. I need their solutions to be similar to each other. However, > > due to various noise in these problems, some of them are not feasible > > or not easy to converge. In result, the output from GLPK for these > > problems are not close to each other at all. One solution I am > > thinking about is to use the solution from one problem as the initial > > solution for the next problem. In this way, even if the next problem > > will not converge, the result might still be close to the initial > > solution. > > > Could anybody tell me how to specify the initial solution for GLPK? Or > > is it possible to do? > > You can do that by specifying the initial basis which the glpk simplex > solver will start from; see api routines glp_set_row_stat and > glp_set_col_stat in the reference manual. However, this will not help > you, because there is no guarantee that the final basis obtained by the > solver will be similar to the initial one. You should formalize what > "to be similar" means in your case. Are solutions x = (0, 1, 0, .5) and > x' = (.5, 0, 1, 0) similar to each other? If so/not, why? > > -- Best wishes, Jinwei Gu
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