### Dana Moshkovitz: An Overview of My PhD

#### My PhD

Ran Raz and I showed that approximating Max-3SAT and other problems exhibit a sharp phase-transition in their computational hardness.

That is, if you assume (exact) 3SAT takes exponential-time 2W(n), and you plot the approximation factor on one axis,

and the time complexity of achieving this approximation on the other axis, then the graph for approximating Max-3SAT looks like this:

In other words, obtaining any approximation better than 7/8+o(1) takes (nearly-)exponential time, while obtaining (7/8)-approximation is polynomial (in fact, linear) time, due to a simple random setting algorithm.

The drop from exponential time to polynomial time is sharp: it occurs at a window of width o(1) at 7/8.

By the work of Håstad in 1997, it was known that any approximation better than 7/8+e of Max-3SAT for a constant e>0 is NP-hard, and the lower bound on the time complexity was 2n^{f(e)} for some fixed increasing function f of e.

This left open the possibility that the computational threshold at 7/8 is coarse. In particular, it was open whether a sub-exponential time algorithm could be given for sufficiently loose constant approximation e<1/8.

Our work eliminated these possibilities, and showed that the computational phase transition is sharp.

Our work is applicable not just for Max-3SAT, but for all of Håstad’s problems, and for many other problems. The reason for the wide-applicability is that we provided an efficient alternative to Raz’s parallel repetition theorem (with a smaller blow-up for any soundness error which is at least 1/(logn)b for some b>0).

So we get better results for every problem whose NP-hardness of approximation is proven by composition of Raz’s verifier (aka Label-Cover/projection games hardness) with the long-code -- the mainstream method today to prove NP-hardness of approximation.

What gets into the proof? For the few years before we obtained this result, we developed techniques for randomness-efficiency in algebraic PCPs. These techniques, together with a new composition method, yielded the result.

The main algebraic problem we considered was low degree testing:

Fix a finite field F and natural numbers m and d, as well as a function f:Fm -> F. If f is a polynomial of degree at most d, then f’s restriction to every line in Fm is a univariate polynomial of degree at most d.

The converse is also true – if the restriction of f to every line is a univariate polynomial of degree at most d, then f is an m-variate polynomial of degree at most d.

The low degree testing theorem says that if the restriction of f to a small fraction of the lines is somewhat close to a univariate polynomial of degree at most d (i.e., there exists a univariate polynomial of degree at most d that agrees with f on a small fraction of the points in the line), then f must be close to an m-variate polynomial of degree at most d.

Rubinfeld-Sudan initiated this study in the 1990’s, and the theorem was proven by Arora-Sudan, 1997 (a similar theorem was proven at around the same time by Raz-Safra). It is the main theorem that goes into the construction of algebraic PCPs.

We devised a small collection of constant-dimensional subspaces, of size |Fm|1+o(1) (as opposed to the quadratic size ~|Fm|2 of the family of all lines in Fm) such that the theorem still holds. Later we showed how to use this theorem to derive randomness efficient PCPs.