Problem Decomposition
10 papers with code • 0 benchmarks • 0 datasets
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Latest papers with no code
Independent RL for Cooperative-Competitive Agents: A Mean-Field Perspective
This MFTG NE is then shown to be $\mathcal{O}(1/M)$-NE for the finite population game where $M$ is a lower bound on the number of agents in each team.
A Composite Decomposition Method for Large-Scale Global Optimization
Furthermore, to enhance the efficiency and accuracy of CSG, we introduce two innovative methods: a multiplicatively separable variable detection method and a non-separable variable grouping method.
Divide-or-Conquer? Which Part Should You Distill Your LLM?
Recent methods have demonstrated that Large Language Models (LLMs) can solve reasoning tasks better when they are encouraged to solve subtasks of the main task first.
Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning
Subsequently, given the fuzzy based client-edge association, a joint edge server scheduling and resource allocation problem is formulated.
Adaptive-Solver Framework for Dynamic Strategy Selection in Large Language Model Reasoning
Experimental results from complex reasoning tasks reveal that the prompting method adaptation and decomposition granularity adaptation enhance performance across all tasks.
LLM Guided Inductive Inference for Solving Compositional Problems
While large language models (LLMs) have demonstrated impressive performance in question-answering tasks, their performance is limited when the questions require knowledge that is not included in the model's training data and can only be acquired through direct observation or interaction with the real world.
Data-driven Topology and Parameter Identification in Distribution Systems with limited Measurements
This manuscript presents novel techniques for identifying the switch states, phase identification, and estimation of equipment parameters in multi-phase low voltage electrical grids, which is a major challenge in long-standing German low voltage grids that lack observability and are heavily impacted by modelling errors.
Hybridization of evolutionary algorithm and deep reinforcement learning for multi-objective orienteering optimization
Multi-objective orienteering problems (MO-OPs) are classical multi-objective routing problems and have received a lot of attention in the past decades.
Incremental Recursive Ranking Grouping for Large Scale Global Optimization
However, if a given problem consists of non-additively separable subproblems, DG-based strategies may discover many non-existing interactions.
Distributed Optimization in Distribution Systems with Grid-Forming and Grid-Supporting Inverters
With massive penetrations of active grid-edge technologies, distributed computing and optimization paradigm has gained significant attention to solve distribution-level optimal power flow (OPF) problems.
