Multi criteria optimization - PowerPoint PPT Presentation

Explore Microsoft's innovative approach to query optimization in production environments, addressing challenges with general-purpose optimization and introducing specialized cloud-based optimizers. Learn about the implementation details, experiments conducted, and the solution proposed. Discover how

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Optimization in process engineering involves obtaining the best possible solution for a given process by minimizing or maximizing a specific performance criterion while considering various constraints. This process is crucial for achieving improved yields, reducing pollutants, energy consumption, an

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Swarm Intelligence (SI) is an artificial intelligence technique inspired by collective behavior in nature, where decentralized agents interact to achieve goals. Swarms are loosely structured groups of interacting agents that exhibit collective behavior. Examples include ant colonies, flocking birds,

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The DNN Inference Optimization Challenge, organized by Liya Yuan from ZTE, focuses on optimizing deep neural network (DNN) models for efficient inference on-device, at the edge, and in the cloud. The challenge addresses the need for high accuracy while minimizing data center consumption and inferenc

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This contribution discusses the reference model to support multi-link operation in IEEE 802.11be and proposes architecture reference models to support multi-link devices. It covers aspects such as baseline architecture reference models, logical entities in different layers, Multi-Link Device (MLD) f

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This document from December 2023 discusses medium synchronization recovery leveraging multi-AP coordination for multi-link devices. It covers features such as Multi-link device (MLD), Multi-link operation (MLO), and Ultra High Reliability (UHR) capability defined in P802.11bn for improvements in rat

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Discrete Optimization is a field of applied mathematics that uses techniques from combinatorics, graph theory, linear programming, and algorithms to solve optimization problems over discrete structures. This involves creating mathematical models, defining objective functions, decision variables, and

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This study delves into the generalization of Empirical Risk Minimization (ERM) in stochastic convex optimization, focusing on minimizing true objective functions while considering generalization errors. It explores the application of ERM in machine learning and statistics, particularly in supervised

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Explore the world of optimization through convex and general problems, understanding the concepts, constraints, and the difference between convex and non-convex optimization. Discover the significance of local and global optima in solving complex optimization challenges.

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Explore the basic components of optimization problems, such as objective functions, constraints, and global vs. local optima. Learn about single vs. multiple objective functions and constrained vs. unconstrained optimization problems. Dive into the statement of optimization problems and the concept

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The study focuses on optimizing multi-label feature selection by balancing global relevance and redundancy factors, aiming to enhance the efficiency and accuracy of data analysis. It delves into the challenges posed by information theoretical-based methods and offers insights on overcoming limitatio

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This document discusses the implementation and advantages of virtual carrier sense in multi-link networks under the IEEE 802.11 standard. It explores the operation of multi-link setups, asynchronous communication benefits, and the necessity of multiple contention channels. The concept of NAV (Networ

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Recent research highlights advancements in solving sampling problems in convex optimization, exemplified by works by Yin Tat Lee and Santosh Vempala. The complexity of convex problems, such as the Minimum Cost Flow Problem and Submodular Minimization, are being unraveled through innovative formulas

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This document explores the performance aspects, benefits, and assumptions of multi-link operations in IEEE 802.11-19/1291r0. It discusses the motivation for multi-link operation in new wireless devices, potential throughput gains, classification of multi-link capabilities, and operation modes. The s

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This piece discusses approximation algorithms for stochastic optimization problems, focusing on modeling uncertainty in inputs, adapting to stochastic predictions, and exploring different optimization themes. It covers topics such as weakening the adversary in online stochastic optimization, two-sta

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In September 2016, a proposal was introduced to enhance the beamforming training procedures in IEEE 802.11ay for increased efficiency and MIMO support. The proposal suggests a multi-stage, multi-resolution beamforming training framework to improve efficiency in scenarios with high-resolution beams a

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This paper proposes innovative methods for multi-server placement and topology control in multi-party video conferences. It introduces a three-step procedure to minimize end-to-end delays between client pairs using D-Grouping and convex optimization. The study demonstrates how combining D-Grouping,

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The document discusses the multi-link operation framework for IEEE 802.11-19/0773r0, focusing on load balancing and aggregation use cases. It introduces terminology related to multi-link logical entities and provides examples of multi-link AP and non-AP logical entities. The framework considers stee

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Real-world design problems often require multi-objective optimization, and the Flower Pollination Algorithm (FPA) developed by Xin-She Yang in 2012 mimics the pollination process of flowering plants to efficiently solve such optimization tasks. FPA has shown promising results in extending to multi-o

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The document discusses IEEE 802.11-17/xxxxr0 focusing on multi-link operation for achieving higher throughput. It covers motions adopted in the SFD related to asynchronous multi-link channel access, mechanisms for multi-link operation, and shared sequence number space. Additionally, it explores the

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The DICOM WG21 Multi-Energy Imaging Supplement aims to address the challenges and opportunities in multi-energy imaging technologies, providing a comprehensive overview of imaging techniques, use cases, objectives, and potential clinical applications. The supplement discusses the definition of multi

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This research presents a hybrid optimization heuristic approach for efficient instruction scheduling in programmable accelerator codesign. It discusses Google's TPU architecture, problem-solving strategies, and computation graph mapping, routing, and timing optimizations. The technique overview high

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This presentation discusses the application of machine learning for optimizing EBIS beam intensity and RHIC luminosity. It covers topics such as motivation, EBIS beam intensity optimization, luminosity optimization, and outlines the plan and summary of the project. Collaborators from MSU, LBNL, and

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Bayesian optimization is being used at LCLS to tune the Free Electron Laser (FEL) pulse energy efficiently. The current approach involves a tradeoff between human optimization and numerical optimization methods, with Gaussian processes providing a probabilistic model for tuning strategies. Prior mea

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The evolution of metalearning in the machine learning community is traced from the initial workshop in 1998 to recent developments in hyper-parameter optimization. Challenges in classifier selection and the validity of hyper-parameter optimization claims are discussed, urging the exploration of spec

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Discussing the connection between Artificial Intelligence (AI)/Machine Learning (ML) and Wireless LAN networks, this document explores how AI/ML can improve IEEE 802.11 features, enhance Wi-Fi performance through optimized data sharing, and enable network slicing for diverse application requirements

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Fast Bayesian Optimization optimizes hyperparameters for machine learning on large datasets efficiently. It involves black-box optimization using Gaussian Processes and acquisition functions. Regular Bayesian Optimization faces challenges with large datasets, but FABOLAS introduces an innovative app

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This presentation delves into the concept of price optimization in the auto insurance industry, covering actuarial, economic, and regulatory aspects. It addresses the controversy surrounding price optimization, various state definitions, concerns, and the use of sophisticated tools to quantify busin

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This content delves into the comparison between Quantum Adiabatic Optimization and Quantum Monte Carlo methods in quantum computing, discussing their approaches, algorithms, potential applications, and theoretical possibilities. It explores the concepts of adiabatic theorem, simulated annealing, sto

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Explore the world of computer systems programming and optimization with ECE 454 at the University of Toronto. Dive into compiler basics, manual optimization, advanced techniques, and more through a comprehensive overview of compiler history. From programmer-machine instructions to high-level languag

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Dive into the world of machine learning optimization with a focus on gradient descent, mathematical programming, and constrained optimization. Explore how to minimize functions using gradient descent and Lagrange multipliers, as well as the motivation behind direct optimization methods. Discover the

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This comprehensive guide delves into various population-based metaheuristics and nature-inspired optimization techniques such as evolutionary algorithms, swarm intelligence, and artificial immune systems. It covers concepts like genetic algorithms, ant colony optimization, particle swarm optimizatio

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Discover the world of multi-objective optimization with NP-hard conflicting objectives, Pareto optimal solutions, and metaheuristics. Learn about fitness assignment, diversity preservation, and dominance-based strategies for finding Pareto optimal sets. Explore hybrid metaheuristics combining variou

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Enhance logical and analytical problem-solving skills in non-linear optimization for decision making. Explore optimization terminology, classification of optimization problems, and various techniques for tackling complex decision-making scenarios.

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Explore the proposal for multi-band/multi-channel operation in IEEE 802.11-19/1615r1 to enhance worst-case latency and jitter performance for real-time data transmission. The document discusses the benefits of multi-link aggregation, requirements for optimization, and channel access methods for impr

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Explore the benefits and models of Multi-Band Multi-Channel (MBMC) operation in IEEE 802.11be, including efficient spectrum use, increased data rates, and dynamic band switching. Learn about single band vs. multi-band operation and different deployment scenarios for APs and non-AP STAs. Discover the

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Explore the relationship between counting techniques, graph theory, and discrete optimization, with examples illustrating the transition from counting problems to optimization problems. Learn about applying optimization in scheduling and making graph models, as well as the role of graphs in discrete

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Explore the advancements in IEEE 802.11-18/1155r1 focusing on Multi-AP enhancements for enhanced coverage and throughput. Learn about cooperative multi-band operations and future possibilities for Wi-Fi networks. Discover the potential for joint multi-AP transmissions and the optimization of MAC and

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Explore the world of multi-objective optimization in industrial engineering through heuristic and metaheuristic approaches. Learn about aggregated weighted sum methods, Pareto optimization, and popular algorithms like MOGA and NSGA-II. Discover how to find trade-offs efficiently in smart industry ap

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Explore the ILP-based co-optimization of cut mask layout, dummy fill, and timing for sub-14nm BEOL technology. The proposed approach addresses self-aligned multiple patterning, cut process extension, and the impact of cut mask optimization on wire performance. Learn about related works, motivation,

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