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Category: optimization

  • A Generalizable MARL-LP Approach for Scheduling in Logistics

    A Generalizable MARL-LP Approach for Scheduling in Logistics Part 1. Hybrid Solution for Dynamic Vehicle Routing — Context and Architecture The post A Generalizable MARL-LP Approach for Scheduling in Logistics appeared first on Towards Data Science. Alexander Levin Go to original source

  • Optimizing Token Generation in PyTorch Decoder Models

    Optimizing Token Generation in PyTorch Decoder Models Hiding host-device synchronization via CUDA stream interleaving The post Optimizing Token Generation in PyTorch Decoder Models appeared first on Towards Data Science. Chaim Rand Go to original source

  • Decisioning at the Edge: Policy Matching at Scale

    Decisioning at the Edge: Policy Matching at Scale Policy-to-Agency Optimization with PuLP The post Decisioning at the Edge: Policy Matching at Scale appeared first on Towards Data Science. Erika Gomes-Gonçalves Go to original source

  • Iron Triangles: Powerful Tools for Analyzing Trade-Offs in AI Product Development

    Iron Triangles: Powerful Tools for Analyzing Trade-Offs in AI Product Development Conceptual overview and practical guidance The post Iron Triangles: Powerful Tools for Analyzing Trade-Offs in AI Product Development appeared first on Towards Data Science. Chinmay Kakatkar Go to original source

  • Overcoming Nonsmoothness and Control Chattering in Nonconvex Optimal Control Problems

    Overcoming Nonsmoothness and Control Chattering in Nonconvex Optimal Control Problems With some hints for good numerics The post Overcoming Nonsmoothness and Control Chattering in Nonconvex Optimal Control Problems appeared first on Towards Data Science. Willem Esterhuizen Go to original source

  • The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs

    The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs An optimal solution to the well-known NP-complete problem, when the input values are close enough to each other. The post The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs appeared first on Towards Data Science. Tigran Hayrapetyan Go to original…

  • How to Analyze and Optimize Your LLMs in 3 Steps

    How to Analyze and Optimize Your LLMs in 3 Steps Learn to enhance your LLMs with my 3 step process, inspecting, improving and iterating on your LLMs The post How to Analyze and Optimize Your LLMs in 3 Steps appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • What Optimization Terminologies for Linear Programming Really Mean

    What Optimization Terminologies for Linear Programming Really Mean Understanding the duality of optimization problem, primal to dual conversion, and the optimality conditions for linear problems. The post What Optimization Terminologies for Linear Programming Really Mean appeared first on Towards Data Science. Himalaya Bir Shrestha Go to original source

  • LLM Optimization: LoRA and QLoRA

    LLM Optimization: LoRA and QLoRA Scalable fine-tuning techniques for large language models The post LLM Optimization: LoRA and QLoRA appeared first on Towards Data Science. Vyacheslav Efimov Go to original source

  • Optimizing Multi-Objective Problems with Desirability Functions

    Optimizing Multi-Objective Problems with Desirability Functions When working in Data Science, it is not uncommon to encounter problems with competing objectives. Whether designing products, tuning algorithms or optimizing portfolios, we often need to balance several metrics to get the best possible outcome. Sometimes, maximizing one metrics comes at the expense of another, making it hard…

  • Linear Programming: Managing Multiple Targets with Goal Programming

    Linear Programming: Managing Multiple Targets with Goal Programming This is the sixth (and likely last) part of a Linear Programming series I’ve been writing. With the core concepts covered by the prior articles, this article focuses on goal programming which is a less frequent linear programming (LP) use case. Goal programming is a specific linear…

  • The Case for Centralized AI Model Inference Serving

    The Case for Centralized AI Model Inference Serving As AI models continue to increase in scope and accuracy, even tasks once dominated by traditional algorithms are gradually being replaced by Deep Learning models. Algorithmic pipelines — workflows that take an input, process it through a series of algorithms, and produce an output — increasingly rely…

  • Efficient Metric Collection in PyTorch: Avoiding the Performance Pitfalls of TorchMetrics

    Efficient Metric Collection in PyTorch: Avoiding the Performance Pitfalls of TorchMetrics Metric collection is an essential part of every machine learning project, enabling us to track model performance and monitor training progress. Ideally, Metrics should be collected and computed without introducing any additional overhead to the training process. However, just like other components of the…

  • Hands-On Delivery Routes Optimization (TSP) with AI, Using LKH and Python

    Hands-On Delivery Routes Optimization (TSP) with AI, Using LKH and Python Here’s how to optimize the delivery routes, from theory to code. Continue reading on Towards Data Science » Piero Paialunga Go to original source

  • Query Optimization for Mere Humans in PostgreSQL

    Query Optimization for Mere Humans in PostgreSQL PostgreSQL: Query Optimization for Mere Humans Understanding a PostgreSQL execution plan with practical examples Photo by Greg Rakozy on Unsplash Today, users have high expectations for the programs they use. Users expect programs to have amazing features, to be fast, and to consume a reasonable amount of resources. As developers,…