| The last time I tried to optimise something I ended up with a column generation formulation. I.e. I was wanting to rapidly iterate between LP solves of the (restricted) master problem & solves of auxiliary problems through hand written problem-specific algorithms taking shadow prices from the master problem dual solution as inputs. Then the auxiliary solution would contribute new variables into the master problem & we'd iterate until hitting a fixed point. I needed shadow prices defined using the master problem dual solution. In my problem instances I would very often run into scenarios where the primal (and hence also dual) master LP problem had a unique objective value but the dual solutions at which that maxima was attained were non-unique. I learned that it only makes sense to talk about shadow prices in some allowable range for each dual decision variable, but LP solvers generally don't give you an API to extract much information about this from the current internal state of the simplex algorithm [0]. I read a bunch of LP solver documentation and the best one I found discussing this kind of stuff was the section in MOSEK's manual about sensitivity analysis[1]. This was for a hobby project, not business, so I didn't try out MOSEK, even though it is looks very modestly priced compared to other commercial packages. What I did find, however, was that some time during the last few years, scipy.optimize grew an LP solver interface, and even better, Matt Haberland contributed a python implementation of an interior-point LP solver straight into scipy [2][3]. I found that Haberland & co's open source interior point LP solver produced dual solutions that tended to be more stable and more useful for shadow prices in my problems than a bunch of the other open source LP solvers I tried (including the various other LP backends exposed by scipy.optimize.linprog). [0] a paper I found very helpful in understand what was going on was Jansen, de Jong, Roos, Terlaky (1997) "Sensitivity analysis in linear programming: just be careful!". In 1997 they got 5 commercial LP solvers to perform a sensitivity analysis on an illustrative toy problem, and although the solvers all agreed on the optimal objective value, none of them agreed with each other on the sensitivity analysis. [1] https://docs.mosek.com/9.2/pythonapi/sensitivity-shared.html [2] https://github.com/scipy/scipy/pull/7123 [3] https://docs.scipy.org/doc/scipy/reference/generated/scipy.o... |