This course describes attempts to answer a fundamental question of CS namely: What can computers solve efficiently?
Some Bibliography:
While the course does not follow one book in a precise order, most of the material can be found in the following two books (that are available online). I will point to relevant section in the books when teaching the material.
Some of the topics (at least in the beginning of the course) are also covered in the following books.
Requirements:
There will be an exam at the end of the course. During the lectures I will sometimes present questions and challenges for you to think about. You are encouraged to work on these questions.
Exercises:
These exercises are not for submission.
However you are strongly encouraged to solve them.
 Exercise 1 (on space complexity)

Exercise 2 (on diagonalization and the polynomial
time hierarchy)

Exercise 3 (on circuits and randomized
algorithms)

An example of a take home exam (from several
years ago). Solving this is a good way to make sure you know the material.

Examples of class exams: (1) (2) (3) (4)
Material:
o The
generality of the argument: the deterministic space hierarchy theorem.
o Why
the argument doesn't give a nondeterministic time hierarchy theorem.
o The
nondeterministic time hierarchy theorem (delayed diagonalization).
o Weakness
of diagnolization: results proven this way hold for
any oracle.
o An
oracle A such that NP^A <> P^A. Implies impossibility of proving
NP<>P by direct diagonalization.
o The
notion of relativizing statements and proofs.
o The
statement NP=P and NP<>P are nonrelativizing.
·
The polynomial time hierarchy
o Definition with quantifiers and definition with oracles
(equivalence is an exercise).
o The hierarchy collapses if PH has a complete problem.
o The hierarchy collapses if Sigma_i
= Pi_i.
o Padding arguments.
o Time space tradeoffs: NTIME(n) not contained in TISP(n^{1.2},n^{0.2}).