Dear Visitor, In this directory you can find some problems which are difficult to solve, at least with interior point methods. Basically, modelling mistakes made these problems "crazy". But, these problems are excelent examples to test numerical robustnes of a solver and observe numerically bad behavior. Here are short stories about these problems: de063155 de063157 These problems comes from an early version of the water management system Aquarius, developed at TU Delft. By building the optimization problem, almost every modelling mistakes were happen, for example wrong measurements which resulted in extreme large and small coefficients. No tolerances were used by the computation of the matrix values. Actually, I found that these problems are unsolvable by most of the solvers. A note: the large values in the RHS are not to be ignored, those constaints are binding at the optimum ! de080285 Similar to the previous two problems. Very small values are presented in the matrix, which "blow-up" the model if scaling is applied. Additionally (which, I believe more strange for IPMs), for some of the free variables a lower bound -10000.0 was introduced. gen gen1 gen2 gen4 These problems are approximations in L1 norm and come from image reconstruction problems. Some of them were created by using fixed point format, which resulted in different relative accuracy in the coefficients (I believe, this is the source of the extreme numerical instability). The problems are additionally degenerate and it is very difficult to solve them to 8-digit accuracy. l30 Originally a convex optimization problem, but formulated as LP. Many free variables, heavy fill-in. iprob Created artifically. All variables are free. Very badly conditioned. Optimal value (by exact aritmetic) has to be 2990.00 ADDITIONS ARE WELCOME ! Csaba Meszaros, meszaros@sztaki.hu