Editing Computational complexity theory (section)

===Hierarchy theorems===
{{main|time hierarchy theorem|space hierarchy theorem}}
For the complexity classes defined in this way, it is desirable to prove that relaxing the requirements on (say) computation time indeed defines a bigger set of problems. In particular, although DTIME(<math>n</math>) is contained in DTIME(<math>n^2</math>), it would be interesting to know if the inclusion is strict. For time and space requirements, the answer to such questions is given by the time and space hierarchy theorems respectively. They are called hierarchy theorems because they induce a proper hierarchy on the classes defined by constraining the respective resources. Thus there are pairs of complexity classes such that one is properly included in the other. Having deduced such proper set inclusions, we can proceed to make quantitative statements about how much more additional time or space is needed in order to increase the number of problems that can be solved.

More precisely, the [[time hierarchy theorem]] states that
<math>\mathsf{DTIME}\big(o(f(n)) \big) \subsetneq \mathsf{DTIME} \big(f(n) \cdot \log(f(n)) \big)</math>.

The [[space hierarchy theorem]] states that
<math>\mathsf{DSPACE}\big(o(f(n))\big) \subsetneq \mathsf{DSPACE} \big(f(n) \big)</math>.

The time and space hierarchy theorems form the basis for most separation results of complexity classes. For instance, the time hierarchy theorem tells us that P is strictly contained in EXPTIME, and the space hierarchy theorem tells us that L is strictly contained in PSPACE.