Computing the Integral R2 Indicator by Perspective Mapping and Box Decomposition
Abstract
The continuous integral R2 indicator is a Pareto-compliant refinement of the classical finite-weight-vector R2 indicator, used in performance assessment, bounded archiving for a-posteriori multi-objective optimization, and skyline selection in databases. This work introduces a bidirectional perspective mapping between continuous integral R2 computation and integration over unions of anchored axis-aligned boxes. After translating the ideal point of a minimization problem to the origin, approximat...
Description / Details
The continuous integral R2 indicator is a Pareto-compliant refinement of the classical finite-weight-vector R2 indicator, used in performance assessment, bounded archiving for a-posteriori multi-objective optimization, and skyline selection in databases. This work introduces a bidirectional perspective mapping between continuous integral R2 computation and integration over unions of anchored axis-aligned boxes. After translating the ideal point of a minimization problem to the origin, approximation points become strictly positive loss vectors, and the subgraph of the lower weighted Tchebycheff envelope over the weight simplex maps to the complement of an anchored-box union in reciprocal objective space. The Jacobian gives an absolute R2 formula as a weighted complement volume with density , while differences of R2 values become finite weighted hypervolume differences. Hence, hypervolume algorithms that emit box decompositions can be reused by replacing ordinary box volumes with closed-form weighted box integrals. For objectives, this gives an output-sensitive overhead for an -box decomposition, or for fixed . Using existing box-decomposition approaches, the integral R2 can be computed in for , in for , and in for , with denoting the size of the approximation set. On the lower-bound side, exact value computation has an lower bound in the algebraic decision-tree model already in two objectives, this bound lifts to every fixed , and exact computation is -hard when is part of the input. Together, the proposed perspective mapping provides a powerful tool for transferring algorithmic and structural results between anchored-box union and hypervolume theory and integral R2 computation.
Source: arXiv:2606.30530v1 - http://arxiv.org/abs/2606.30530v1 PDF: https://arxiv.org/pdf/2606.30530v1 Original Link: http://arxiv.org/abs/2606.30530v1
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Jun 30, 2026
Mathematics
Mathematics
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