Scaling

# Scaling Scaling is a very important issue to consider in mathematical computations on a computer. A computer stores floating-point numbers with a limited precision because it uses a fixed number of bytes to store them. Consider for example the result of the following calculation: 1/3 In our metric system, the result of this computation cannot be represented with a finite number of digits. The value is 0.3333333333333... with an infinite number of digits. Because a computer only has a certain precision, the result is stored with a finite number of digits. For regular floating-point numbers this is about 15 digits, independent of its size. So this result would for example be stored as 0.333333333333333 on a computer. The computation of 100000000000000000/3 would be stored as 33333333333333300. In several cases, this is no problem. The stored value is close enough to the value that is needed. However, when doing calculations, problems can arise because of this. Especially if small and large values are added or subtracted from each other. In that case, the precision of the small number is lost. This can result in the effect that calculated values aren't correct anymore. The more floating-point calculations are done, the more chance there is that numerical instability occurs and doing calculations with big and small numbers results in faster occurrence of this effect. Then there is also the effect of accumulating rounding errors which can result in a totally screwed up data matrix after having done several calculations. The simplex algorithm is a typical iterating process where factors of thousands of floating calculations are done to find the optimal solution. The chance of numerical instability is then also quite big. But scaling not only improves numerical stability and minimizes rounding errors, it also improves performance. This may seem strange, but it is true. When a model is not scaled, the algorithm could reject certain pivot elements because they are too small and because of this, the solver doesn't choose the shortest route to the solution. If a model is proper scaled, this effect will not occur. There are several ways to cope with this and it starts with the input data. The chance for numerical instability and rounding errors is considerably larger when the input data contains both large and small numbers. So to improve stability, one must try to work with numbers that are somewhat in the same range. Ideally in the neighbourhood of 1. You should realize, that you the user are probably in a better position to scale the problem than any computer algorithm. The way to do this is: first choose appropriately scaled units for the extensive variables of the problem, e.g., kilo-tons instead of pounds, tankcar loads instead of gallons, millions of dollars instead of pennies, or nanometers instead of feet, so that the numerical spread of the coefficients in each single constraint, and in the objective function, will be at most one or two orders of magnitude; and then second scale the constraints themselves, by multiplying each entire constraint relation by an appropriate positive constant, so that the numerical spread of the coefficients in the different constraints is at most another one or two orders of magnitude. For example saying: 10000 x1 + 20000 x2 <= 30000 Can also be expressed as: 1 x1 + 2 x2 <= 3 lp_solve also has some build-in scaling routines which can take over this scaling job from the modeller or maybe better, to do additional scaling. This is implemented in such a way that it is transparent to the user. When scaling is done, lp_solve scales rows and/or columns, but the user doesn't see anything of this process. The returned values are still as if no scaling was done. See set_scaling for the possible scaling types. Note that lp_solve, by default doesn't do scaling. It must be invoked explicitly. Also note that even when scaling is enabled that integer variables are then again by default not scaled. To scale also integer variables, SCALE_INTEGERS must be added to the scale mode. This is done because of the nature of integer variables. If you want them integer, but you also scale them, then they aren't integer anymore. It is possible to scale those variables with the SCALE_INTEGERS flag, but some accuracy on being integer can be lost. Conslusion. You should always do some sort of scaling. It starts when you design the model. Extra scaling can be accomplished by one of the scaling options of lp_solve.