Proves that ℓ_p norm minimization yields p-independent Hausdorff convergence rate O(k^{2/(1-q)}) in convex vector optimization via Euclidean intermediary and norm equivalence.
L¨ ohne,Vector Optimization with Infimum and Supremum(Vector Optimization), en
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Introduces parallel subproblem evaluation and batch addition of up to K cuts per iteration for a convex vector optimization algorithm, proves the batch variant preserves the O(k^{2/(1-q)}) convergence rate, and reports 62-80% fewer iterations with variable wall-clock gains.
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Convergence Rates for $\ell_p$ Norm Minimization in Convex Vector Optimization
Proves that ℓ_p norm minimization yields p-independent Hausdorff convergence rate O(k^{2/(1-q)}) in convex vector optimization via Euclidean intermediary and norm equivalence.
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On Parallel and Batch-Cutting Strategies for Norm-Minimization-Based Convex Vector Optimization
Introduces parallel subproblem evaluation and batch addition of up to K cuts per iteration for a convex vector optimization algorithm, proves the batch variant preserves the O(k^{2/(1-q)}) convergence rate, and reports 62-80% fewer iterations with variable wall-clock gains.