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Let L be a set of n axis-parallel lines in R3. We are are interested in partitions of R3 by a set H of three planes such that each open cell in the arrangement A(H) is intersected by as few lines from L as possible. We study such partitions in three settings, depending on the type of splitting planes that we allow. We obtain the following results. * There are sets L of n axis-parallel lines such that, for any set H of three splitting planes, there is an open cell in A(H)  that intersects at least ⌊n/3⌋ - 1 ≈ n/3 lines. * If we require the splitting planes to be axis-parallel, then there are sets L of n axis-parallel lines such that, for any set H of three splitting planes, there is an open cell in A(H) that intersects at least (3/2) ⌊n/3⌋ - 1 ≈ (1/3 + 1/24) n lines. Furthermore, for any set L of n axis-parallel lines, there exists a set H of three axis-parallel splitting planes such that each open cell in A(H) intersects at most (7/18) n = (1/3 + 1/18) n lines. * For any set L of n axis-parallel lines, there exists a set H of three axis-parallel and mutually orthogonal splitting planes, such that each open cell in A(H) intersects at most ⌈5/12 n⌉ ≈ (1/3 + 1/12) n lines. 

Let T be a set of n planar semi-algebraic regions in R^3 of constant complexity (e.g., triangles, disks), which we call _plates_. We wish to preprocess T into a data structure so that for a query object gamma, which is also a plate, we can quickly answer various intersection queries, such as detecting whether gamma intersects any plate of T, reporting all the plates intersected by gamma, or counting them. We also consider two simpler cases of this general setting: (i) the input objects are plates and the query objects are constant-degree algebraic arcs in R^3 (arcs, for short), or (ii) the input objects are arcs and the query objects are plates in R^3. Besides being interesting in their own right, the data structures for these two special cases form the building blocks for handling the general case. By combining the polynomial-partitioning technique with additional tools from real algebraic geometry, we obtain a variety of results with different storage and query-time bounds, depending on the complexity of the input and query objects. For example, if T is a set of plates and the query objects are arcs, we obtain a data structure that uses O^*(n^(4/3)) storage (where the O^*(...) notation hides subpolynomial factors) and answers an intersection query in O^*(n^(2/3)) time. Alternatively, by increasing the storage to O^*(n^(3/2)), the query time can be decreased to O^*(n^(rho)), where rho = (2t-3)/(3(t-1)) < 2/3 and t≤3 is the number of parameters needed to represent the query arcs. 

We describe a dynamic data structure for approximate nearest neighbor (ANN) queries with respect to multiplicatively weighted distances with additive offsets. Queries take polylogarithmic time, while the cost of updates is amortized polylogarithmic. The data structure requires near-linear space and construction time. The approach works not only for the Euclidean norm, but for other norms in ℝ^d, for any fixed d. We employ our ANN data structure to construct a faster dynamic structure for approximate SINR queries, ensuring polylogarithmic query and polylogarithmic amortized update for the case of non-uniform power transmitters, thus closing a gap in previous state of the art. To obtain the latter result, we needed a data structure for dynamic approximate halfplane range counting in the plane. Since we could not find such a data structure in the literature, we also show how to dynamize one of the known static data structures. 

We present subquadratic algorithms in the algebraic decision-tree model for several 3Sum-hard geometric problems, all of which can be reduced to the following question: Given two sets A, B, each consisting of n pairwise disjoint segments in the plane, and a set C of n triangles in the plane, we want to count, for each triangle Δ ∈ C, the number of intersection points between the segments of A and those of B that lie in Δ. The problems considered in this paper have been studied by Chan (2020), who gave algorithms that solve them, in the standard real-RAM model, in O((n²/log²n) log^O(1) log n) time. We present solutions in the algebraic decision-tree model whose cost is O(n^{60/31+ε}), for any ε > 0. Our approach is based on a primal-dual range searching mechanism, which exploits the multi-level polynomial partitioning machinery recently developed by Agarwal, Aronov, Ezra, and Zahl (2020). A key step in the procedure is a variant of point location in arrangements, say of lines in the plane, which is based solely on the order type of the lines, a "handicap" that turns out to be beneficial for speeding up our algorithm. 

We prove that some exact geometric pattern matching problems reduce in linear time to 𝑘-SUM when the pattern has a fixed size 𝑘. This holds in the real RAM model for searching for a similar copy of a set of 𝑘≥3 points within a set of n points in the plane, and for searching for an affine image of a set of 𝑘≥𝑑+2 points within a set of n points in d-space. As corollaries, we obtain improved real RAM algorithms and decision trees for the two problems. In particular, they can be solved by algebraic decision trees of near-linear height.