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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
TRANS outperforms MTF for two special types of request se...
Rakesh Mohanty, Sangita Patel, Shiba Prasad Dash, Burle Sharma · 2013-09-10 · via cs.DS updates on arXiv.org

Various list accessing algorithms have been proposed in the literature and their performances have been analyzed theoretically and experimentally. Move-To-Front (MTF) and Transpose (TRANS) are two well known primitive list accessing algorithms. MTF has been proved to be the best performing online algorithm till date in the literature for real life inputs and practical applications with locality of reference. It has been shown that when storage space is extremely limited and pointers for lists cannot be used, then array implementation of TRANS gives efficient reorganization. Use of MTF is extensive in the literature whereas, the use of TRANS is rare. As mentioned as an open problem in literature, direct bounds on the behavior and performance of various list accessing algorithms are needed to allow realistic comparisons. Since it has been shown that no single optimal permutation algorithm exists, it becomes necessary to characterize the circumstances that indicate the advantage in using a particular list accessing algorithm. Motivated by above challenging research issue, in this paper we have made an analytical study for evaluating the performance of TRANS list accessing algorithm using two special types of request sequences without locality of reference. We have compared the performance of TRANS with MTF and observed that TRANS outperforms MTF for these considered types of request sequences.