This paper proposes a learning-based framework, CBA-BO, for solving expensive constrained optimization problems with continuously varying threshold settings by learning a parametric constraint model.

The paper establishes conditions for QUBO formulations of combinatorial optimization problems that guarantee valid binary and feasible local minimizers using gradient-based methods.

The paper introduces local Preferential Bayesian Optimization (PBO) methods that adapt high-dimensional Bayesian Optimization techniques, such as trust-region and derivative-informed local search, to…

This paper proposes a Hybrid Augmented Lagrangian (HyAL) method that integrates the constraint-handling strengths of the AL framework with the exploratory power of population-based search.

The paper proposes graph-coupled causal Bayesian optimization, a method that improves efficiency by sharing information across related interventions through a shared set of causal parameters.

The paper introduces Opt-Verifier, a novel LLM-based framework that significantly improves the accuracy of automated optimization model generation by implementing dual-side verification from both stru…

This paper analyzes the performance and explainability requirements of evolutionary algorithms when applied to complex, real-world physics-informed optimization problems, identifying a gap between cur…

This paper presents a polynomial-time algorithm for recovering item values in the fractional knapsack problem using comparison queries, and provides a lower bound.

The paper proposes an objective-wise reputation-market mechanism to dynamically calibrate and gate LLM-generated expert priors in multi-objective Bayesian optimization, showing that dynamic calibratio…

This paper proposes a Multi-stage Constrained Optimization Framework (MCOF) for Variational Autoencoders (VAEs) to address challenges in sampling, identifying active decision variables, and enforcing…

This paper proposes a four-layer technical architecture for large model inference optimization, including Multi-model Fusion, Model Optimization, Compute-Model Fusion, and Compute-Network-Model Fusion…

The paper develops a general framework for dynamic consistent submodular maximization, achieving constant-factor approximations with sublinear consistency for both cardinality and rank-$k$ matroid con…

The paper develops an explicit multi-linear polynomial form for binary polynomial optimization problems after eliminating a subset of variables, allowing for characterization of new special classes wi…

The paper introduces Posterior Hybrid Bayesian Belief (PhyB), a novel framework that reformulates policy optimization in Bayesian Offline RL by approximating expectations as a convex combination over…

The paper introduces MINTS, a minimalist Bayesian framework that simplifies sequential decision-making by placing priors only on the optimum location, allowing for the incorporation of structural cons…

This paper proposes a randomized iterative method called Sequential Preconditioned Conjugate Gradient Method (SPCG) for large-scale linear statistical models, which significantly reduces computational…

This paper introduces Neural Certificate Pricing (NCP), an unsupervised learning framework that exploits the asymmetry between certifiable discrete structures and structural feasibility in combinatori…