Proceedings Article10.1109/ICTAI.2018.00094
Unranking Combinations Using Gradient-Based Optimization
Victor Parque,Tomoyuki Miyashita +1 more
- 13 Dec 2018
- pp 579-586
9
TL;DR: The proposed approach offers the building blocks to enable the succinct modeling and the efficient optimization of combinatorial structures and decreases with m in average, implying the attractive scalability in terms of m.
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Abstract: Combinations of m out of n are ubiquitous to model a wide class of combinatorial problems. For an ordered sequence of combinations, the unranking function generates the combination associated to an integer number in the ordered sequence. In this paper, we present a new method for unranking combinations by using a gradient-based optimization approach. Exhaustive experiments within computable allowable limits confirmed the feasibility and efficiency of our proposed approach. Particularly, our algorithmic realization aided by a Graphics Processing Unit (GPU) was able to generate arbitrary combinations within 0.571 seconds and 8 iterations in the worst case scenario, for n up to 1000 and m up to 100. Also, the performance and efficiency to generate combinations are independent of n, being meritorious when n is very large compared to m, or when n is time-varying. Furthermore, the number of required iterations to generate the combinations by the gradient-based optimization decreases with m in average, implying the attractive scalability in terms of m. Our proposed approach offers the building blocks to enable the succinct modeling and the efficient optimization of combinatorial structures.
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Citations
Unranking Small Combinations of a Large Set in Co-Lexicographic Order
TL;DR: A modification of Ruskey’s algorithm for unranking m-combinations of an n-set in co-lexicographic order is developed based on the use of approximations to make a preliminary search for the values of the internal parameter k of this algorithm.
Generalized Sparse Regression Codes for Short Block Lengths
Madhusudan Kumar Sinha,Arun Pachai Kannu +1 more
TL;DR: GSPARC improves block error performance of sparse regression code in short block length regime over AWGN channel using Gold sequences and MUB based dictionary matrices.
1
Context-Adaptive Sub-Nyquist Sampling for Low-Power Wearable Sensing Systems
TL;DR: In this article , a context-adaptive sample acquisition strategy at sub-Nyquist sampling rate for wearable embedded sensor devices is proposed to minimise sampling and transmission costs, which can be applied to compressive sensing frameworks to reduce energy consumption.
Generalized Sparse Regression Codes for Short Block Lengths
Madhusudan Kumar Sinha,Arun Pachai Kannu +1 more
- 01 Aug 2023
TL;DR: GSPARC is a sparse regression code that improves the block error performance of SPARC in short block length regime over the AWGN channel. It introduces suitable candidates for dictionary matrices, proposes two generalizations of SPARC, and develops a greedy decoder called Match and Decode (MAD) algorithm.
1
On Vehicle Evaluation and Design Using Data Envelopment Analysis with Hierarchical Concepts
Victor Parque,Kazuhiro Honobe,Satoshi Miura,Tomoyuki Miyashita +3 more
- 01 Jul 2019
TL;DR: A hybrid framework in which evaluation models are generated by integrating Interpretive Structural Modeling, Hierarchical Clustering and Data Envelopment Analysis (DEA) to generate the optimal vehicle evaluation metric is proposed.
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