Lower bounds on the worst-case complexity of some oracle algorithms

To give a proper definition of the complexity of very general computational problems such as optimization problems over arbitrary independence systems or fixed-point problems for continuous functions, it is useful to consider the input for these problems as “oracles” R which can be called by the algorithms for some values x ∈ X and which then give back some information R(x) about x, e.g. whether x belongs to the independence system or the point into which x is mapped by the continuous function. A lower bound on the complexity of an algorithm using an oracle R is the number of calls on R in the worst case. Using this technique it is shown that there is no polynomial approximative algorithm for the maximization problem over a general independence system which has a better worst-case behaviour than the greedy algorithm. Moreover several formalizations of the problem of approximating a fixed point of a continuous function are considered, and it is shown that none of these problems can be solved by a bounded algorithm.