In this chapter, we use polynomial methods to study incidence-related problems in spaces over finite fields. We focus on two breakthroughs: A solution to the finite field Kakeya problem and the cap set problem. The proofs of these results are short, elegant, and require mostly elementary tools. In Chapter 13, we study point-line incidences in spaces over finite fields, which require more involved arguments.

This chapter contains a variety of other interesting problems and tools. We study the method of multiplicities, which improves the constant of the finite-field Kakeya theorem. To study the cap set problem, we use the slice rank technique. This technique is also used to obtain bounds for the 3-sunflower problem. As a warm-up towards the slice rank technique, we consider the Odd town problem and the two distance problem.

Before their seminal distinct distances paper, Guth and Katz wrote another paper that introduced a new polynomial method. In this chapter, we study one of the two problems that were resolved in that paper: the joints problem. The solution to this problem relies on a simple polynomial technique, which is based on polynomial interpolation. This is also a good warm-up for working in spaces of dimension larger than two.

We use the polynomial interpolation technique to study two additional problems. First, we study the sets in R^3 that are formed by the union of all lines that intersect three pairwise-skew lines. We then use the degree reduction technique to study polynomial interpolation of lines.

It is usually easier to study problems over the complex than over the reals. Discrete geometry problems are an exception, often being significantly simpler over the reals. While there are several simple proofs of the Szemerédi–Trotter theorem over the reals, we only have rather involved proofs for the complex variant of the theorem. To avoid such involved proofs, we prove a slightly weaker variant of the complex Szemerédi–Trotter theorem. Our analysis is based on thinking of C^2 as R^4.

In Chapter 7, we began to prove the distinct distances theorem by studying the ESGK framework. We complete this proof in Chapter 9, by relying on the constant-degree polynomial partitioning technique. In the current chapter we introduce this technique by studying incidences with lines in the complex plane. This is a warm-up towards Chapter 9, where we use constant-degree polynomial partitioning in more involved ways.

In this chapter, we study our first new polynomial technique: polynomial partitioning. We first see the polynomial partitioning theorem. We use this theorem to derive an incidence bound between points and curves in the real plane. This bound generalizes the Szemerédi–Trotter theorem and the current best bound for the unit distances problem. In the second part of the chapter, we prove the polynomial partitioning theorem by using the ham sandwich theorem and Veronese maps. Finally, we use the point-curve incidence bound to obtain an upper bound for the number of lattice points that a curve can contain.

During the chapter we learn other important concepts, such as Warren’s theorem, incidence graphs, and various tricks for working with curves.

In this chapter we discuss advanced tools and techniques, which rely on additional concepts from algebraic geometry. These tools could be helpful for people who do research work in incidence theory and related topics. A reader who is new to this field might prefer to skip this chapter.

We sometimes wish to consider families of varieties, such as the set of circles in the plane or the set of planes in R^3 that not are incident to the origin. In this chapter, we rigorously define such families. We also generalize the idea of point-line duality to every family of varieties. We then see how these notions could be used to prove various results. In particular, we derive a new incidence bound and prove various properties of surfaces in R^3 and C^3.

After the long and technical proof of the distinct distances theorem, we move to a lighter chapter. In this chapter we study two additional distinct distances problems. We first show that every planar point set contains a large subset that does not span any distance more than once. We then study the structural distinct distances problem: characterizing the point sets that span a small number of distinct distances.

We also study a problem that does not involve distinct distances, but relies on a variant of Theorem 9.2. This problem considers sets of intervals in the plane that span many trapezoids.

In Chapter 7 we studied the ESGK framework. This was a reduction from the distinct distances problem to a problem about pairs of intersecting lines in R^3. In the current chapter we further reduce the problem to bounding the number of rich points of lines in R^3. We solve this incidence problem with a more involved variant of the constant-degree polynomial partitioning technique. This completes the proof of the Guth–Katz distinct distances theorem.

The original proof of Guth and Katz is quite involved. We study a simpler proof for a slightly weaker variant of the distinct distances theorem. This simpler proof was introduced by Guth and avoids the use of tools such as flat points and properties of ruled surfaces.

In Chapter 11 we derived general point-variety incidence bounds in R^d. We now study two applications of these bounds. These applications do not require reading any part of Chapter 11, except for the statement of Theorem 11.3. The first application comes from discrete geometry and is another distinct distances problem. Specifically, it is a distinct distances problem with local properties. The second application is a discrete Fourier restriction problem from harmonic analysis. For simplicity, we only discuss the combinatorial aspect of that problem. This aspect is the additive energy of points on a hypersphere.