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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
On Succinct Representations of Binary Trees
Pooya Davoodi, Rajeev Raman, Srinivasa Rao Satti · 2014-10-18 · via cs.DS updates on arXiv.org

We observe that a standard transformation between \emph{ordinal} trees (arbitrary rooted trees with ordered children) and binary trees leads to interesting succinct binary tree representations. There are four symmetric versions of these transformations. Via these transformations we get four succinct representations of $n$-node binary trees that use $2n + n/(\log n)^{O(1)}$ bits and support (among other operations) navigation, inorder numbering, one of pre- or post-order numbering, subtree size and lowest common ancestor (LCA) queries. The ability to support inorder numbering is crucial for the well-known range-minimum query (RMQ) problem on an array $A$ of $n$ ordered values. While this functionality, and more, is also supported in $O(1)$ time using $2n + o(n)$ bits by Davoodi et al.'s (\emph{Phil. Trans. Royal Soc. A} \textbf{372} (2014)) extension of a representation by Farzan and Munro (\emph{Algorithmica} \textbf{6} (2014)), their \emph{redundancy}, or the $o(n)$ term, is much larger, and their approach may not be suitable for practical implementations. One of these transformations is related to the Zaks' sequence (S.~Zaks, \emph{Theor. Comput. Sci.} \textbf{10} (1980)) for encoding binary trees, and we thus provide the first succinct binary tree representation based on Zaks' sequence. Another of these transformations is equivalent to Fischer and Heun's (\emph{SIAM J. Comput.} \textbf{40} (2011)) \minheap\ structure for this problem. Yet another variant allows an encoding of the Cartesian tree of $A$ to be constructed from $A$ using only $O(\sqrt{n} \log n)$ bits of working space.