We study online bin packing with per-bin maximum delay: each sealed bin incurs a unit opening cost plus the longest waiting time among its items. Offline, this becomes a temporal-span packing objective. We prove strong NP-hardness and rule out absolute approximation factors below three halves unless P equals NP. We com...
We study online covering in which each instantaneous service pays its purchase cost and one maximum waiting time, with no effect on future requests. For static realizable services, monotone subadditivity suffices for optimal competitive ratios; submodularity is unnecessary. A normalized monotone subadditive lower-bound...
The deterministic guarantee matches the known fixed-node lower bound, and the matching randomized lower bound are proved, ensuring that both guarantees are optimal on every nondegenerate rooted tree.
Tian-Han Lu, Run-Tian Ren, Sheng-Cai Liu et al.· 3 citations
We study online service with one maximum-waiting-time charge per service batch. The persistent server endpoint prevents a phase-by-phase comparison with the offline optimum: an offline schedule may merge many online phases, share movement globally, and finish at unrelated endpoints. Our main contribution is a metric-in...
Tian-Han Lu, Run-Tian Ren, Sheng-Cai Liu et al.· 0 citations
High-performance ML systems increasingly rely on GPU kernels whose editable source is unavailable, generated, or too distant from final machine code to expose remaining optimizations. Existing LLM kernel optimizers and autotuners mainly operate on CUDA, Triton, HIP, or tensor-program source and validate against referen...
Ji Liu, Puyu Yang, Rongzhang Zheng et al.· 0 citations
A cross-fidelity knowledge distillation and adaptive fusion network (CFKD-AFN), which leverages abundant but low-fidelity simulation data to enhance the prediction on scarce but high-fidelity trial data, and is extended to an interpretable variant for exploratory analysis of feature-attribution patterns associated with...
Wen-Jing Chen, Lian-Sheng Zhuang, Zi-Ying Luo et al.· arXiv.org· 0 citations
CoPES is introduced, a cooperative coevolutionary method that decomposes the full parameter space into lower-dimensional subspaces and searches over them cooperatively to improve optimization efficiency and demonstrate an improved trade-off between memory requirements and training time for agentic LLM post-training und...
Zhiyuan Wang, Sheng-Cai Liu, Jiahao Wu et al.· 2 citations
EMO combines graph analysis and pack-level models to formulate energy optimization as a constrained combinatorial problem, efficiently solving for optimal frequency policies under given latency targets.
Jiyu Luo, Shaoyu Chen, Jingwei Sun et al.· 0 citations
OptiDSL is proposed, a framework that shifts the focus from rigid MILP formulations to domain-specific language (DSL) representations, and enables seamless integration with a diverse library of specialized solvers, ranging from traditional heuristics to modern learning-based methods.
Shao-Feng Zhang, Hongyuan Su, Qing Peng et al.· 0 citations
The potential gain metric is proposed, a novel metric that eliminates the need for reference solutions and consistently outperforms state-of-the-art LLM-ACP baselines, notably achieving a 19.76% relative improvement for TSP Greedy Constructive portfolios.
Shaofeng Zhang, Shengcai Liu, Zhiyuan Wang et al.· 0 citations
Large language model-based automated heuristic design (LLM-AHD) has shown strong potential in discovering effective heuristics for combinatorial optimization problems. However, existing methods primarily optimize a single heuristic, whereas practical optimization frameworks often rely on multiple interacting components...
Haoze Lv, Ning Lu, Shengcai Liu et al.· 0 citations
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