REVIEW 5 cited by
Smooth Tchebycheff Scalarization for Multi-Objective Optimization
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Multi-objective optimization problems can be found in many real-world applications, where the objectives often conflict each other and cannot be optimized by a single solution. In the past few decades, numerous methods have been proposed to find Pareto solutions that represent optimal trade-offs among the objectives for a given problem. However, these existing methods could have high computational complexity or may not have good theoretical properties for solving a general differentiable multi-objective optimization problem. In this work, by leveraging the smooth optimization technique, we propose a lightweight and efficient smooth Tchebycheff scalarization approach for gradient-based multi-objective optimization. It has good theoretical properties for finding all Pareto solutions with valid trade-off preferences, while enjoying significantly lower computational complexity compared to other methods. Experimental results on various real-world application problems fully demonstrate the effectiveness of our proposed method.
Forward citations
Cited by 5 Pith papers
-
MAdam: Metric-Aware Multi-Objective Adam
MAdam preconditions MOO solver directions with preference-conditioned curvature so that Adam's adaptive steps respect the intended metric instead of entangling it with gradient history.
-
Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization
An adaptive smooth Tchebycheff controller for multi-objective RL lets agents reach non-convex Pareto regions in robotic tasks while avoiding the instability of static non-linear scalarizations.
-
MOSAIC: Adaptive Inter-layer Composition for Efficient Heterogeneous Vision-Language Models
MOSAIC uses multi-objective MIP search over linear/sparse/low-rank operators plus two-stage distillation to convert a homogeneous VLM into a hardware-aware heterogeneous model that matches teacher performance at 2.5× ...
-
SURF: Steering the Scalarization Weight to Uniformly Traverse the Pareto Front
SURF derives weight sampling rules from the arc-length CDF of the scalarization path to uniformly traverse the Pareto front in multi-objective optimization.
-
AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics
AutoScale selects fixed linear-scalarization weights by optimizing multi-task optimization metrics during a short exploration phase, matching grid-searched performance without search.
Discussion (0). Sign in to comment.