An Improved Algorithm to Select Homotopy Candidates for the Constrained Optimization of Minimum-Power Networks

We present a strategy to extract an optimal subset—i.e., a subset that contains the homotopy of the optimal solution— from the complete set of homotopies of a minimum-power network in the presence of obstacles. The strategy only requires knowing the optimal solution in the absence of obstacles (the "free" solution) and the details of the obstacle distribution. The homotopy candidates are obtained by overlaying the free solution on the obstacle distribution and analyzing the generated intersections.

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Publication Details

Published
2025-08-17
DOI
https://doi.org/10.1109/case58245.2025.11163892
Primary Topic
Education, Psychology, and Social Research
Type
article
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article

An Improved Algorithm to Select Homotopy Candidates for the Constrained Optimization of Minimum-Power Networks

Education, Psychology, and Social Research
article

An Improved Algorithm to Select Homotopy Candidates for the Constrained Optimization of Minimum-Power Networks

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Abstract

We present a strategy to extract an optimal subset—i.e., a subset that contains the homotopy of the optimal solution— from the complete set of homotopies of a minimum-power network in the presence of obstacles. The strategy only requires knowing the optimal solution in the absence of obstacles (the "free" solution) and the details of the obstacle distribution. The homotopy candidates are obtained by overlaying the free solution on the obstacle distribution and analyzing the generated intersections.

University of Houston (US)
Openalex Percentile: Top 36%
Education, Psychology, and Social Research
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An Improved Algorithm to Select Homotopy Candidates for the Constrained Optimization of Minimum-Power Networks · (2025) | TGRS Research Map | TGRS