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🧮 Optimization of Distributed Networks Using Multi-Scalar Tensors
This research introduces a new paradigm based on multi-scalar tensor algebra to optimize resource allocation in distributed systems such as logistics, parallel processing, or decentralized cloud infrastructures.
📌 Objective
The model uses tensor-based formulations to allow parallel evaluation, layered execution and minimal delay in computation across nodes.
🧠 Core Concepts
- Multi-scalar state tensors to manage asynchronous communication
- Node priority matrices for optimal task flow
- Tensor contraction to model convergence zones
⚙️ Mathematical Representation
T(i, j, t) = Σ_k A(i, k) * B(k, j) * φ(t)
where T is the task flow tensor, A and B represent inter-node connectivity, and φ(t) encodes time-varying load functions.
📈 Application
This system was tested on a simulated logistics network to optimize delivery flow and reduce processing delay. The model was shown to outperform traditional queue-based approaches in real-time adaptability.
📊 Results
The use of dynamic tensors allowed simultaneous path correction across multiple layers of the network. An increase of 26% in global efficiency was observed in parallel execution scenarios.