Interval analysis is a very specific domain and require some expertise to be efficient. We give in this section some tricks that may help you.
As a naive user you may want to solve a specific problem but the problem that you have submitted to ALIAS may be already a transformed version of the problem you want really to solve and this transformation may be not favorable to the use of interval analysis. Focusing on the problem you want to solve is important for the use of interval analysis.
Rule: focus on the problem you want to solve
Let us give you an example: you have a third order polynomial whose coefficients depend on a set of parameters and you want to find if there are values of these parameters such that the root of the polynomial verifies some properties. A natural way of doing that is to use the available generic analytical form of the root and check if you may find parameters such that satisfy the properties. In terms of interval analysis you have already transformed your problem and this may not be a good idea (the analytical form of the roots of a 3rd order polynomial is badly conditioned).
Rule: it is usually a bad idea to try to adapt to interval analysis the last step of a solving problem that has already been transformed to fit another method
Thinking backward what you want really is to verify if the satisfying satisfy the properties. Hence you should use as unknown not only the but also the : you add an unknown but the calculation will then involve only the evaluation of the polynomial which is much more simple than the evaluation of the analytical form of the roots. Note that ALIAS C++ provides procedures to determine bounds for the unknown .
Rule: introducing new variables for simplifying expressions may be a good strategy
See section 11.2.2 for another example on how focusing on the problem may help to solve it efficiently. A good strategy is also to learn what are the main problems that have been addressed in interval analysis so that you can later on transform your problem into another one for which solving algorithms are available.
Rule: investigate which main problems have been addressed in interval analysis so that you may later on transform your problem into another one for which solving algorithms are available
Typical of such approach is to determine if a parametric matrix may include singular matrices. You may be tempted to derive the determinant of the interval matrix and then to determine if there are values of the unknowns that cancel it. But if you have not been able to manually determine these values this probably means that the determinant is quite complicated, and therefore not appropriate for interval analysis. But determining if a parametric matrix may include a singular matrix is a well-known problem in interval analysis and for solving this problem there are efficient methods that do not require the calculation of the determinant (see section 220.127.116.11).
Another case of failure occurs when the system is badly conditioned. This may occur, for example, when you have large coefficients in an equation while the values of the variable are very small: as ALIAS takes into account the round-off errors you will get large interval evaluation even for a small width of the intervals in the input interval. There is no standard way to solve the problem. The first thing you may try is to normalize the unknowns in order to reduce the size of the coefficients. A second approach may be to switch the solving problem to an optimization problem.
Another trick for system solving is to combine the initial equations to get new one. For example consider the system:
cons1 := a1 + a2 + a3 + 1 ; cons2 := a1 + 2*a2*x1 + 3*a3*x1^2 + 4*x1^3 ; cons3 := a1 + 2*a2*x2 + 3*a3*x2^2 + 4*x2^3 ; cons4 := a1 + 2*a2*x3 + 3*a3*x3^2 + 4*x3^3 ; cons5 := a1*(x1+x2) + a2*(x1^2 + x2^2) + a3*(x1^3 + x2^3) + x1^4 + x2^4 ; cons6 := a1*(x2+x3) + a2*(x2^2 + x3^2) + a3*(x2^3 + x3^3) + x2^4 + x3^4 ; cons7 := x1 + delta -x2<= 0; cons8 := x2 + delta -x3<= 0;(which is the fredtest example of our Web tests page). As it this system is difficult to solve (the computation time is over 10 minutes with a 3B increment of 0.001). Now we may notice that monomials involving a1 appear in many equations. Hence we consider adding as equations cons2-cons1, cons2-cons3 and so on. With this additional equations the computation time reduces to about 1mn 20 seconds.
Rule:Very often introducing additional constraints derived from the set of initial ones will reduce the solving time
Interval arithmetics plays evidently a large role in the computation time. The interval evaluation of mathematical operator such as sine, cosine,...is relatively expensive. If similar complex expressions appear more than once in an expression it is usually very efficient to compute it only once and to substitute it in the other places (see section 2.1.2 for a procedure that allows this manipulation).
Rule:try to avoid multiple interval evaluation of the same complex expressions