Global ETD Search

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Showing 1 to 4 of 4 for “"Constrained convex optimization"”.

  1. Simulation- and Experiment-Based Setpoint Control for Heating, Ventilation, and Air-Conditioning Systems: A Single- and Multi-Objective Optimization Problem

    … air quality. The problem is formulated as a constrained convex optimization statement. Specifically, the thesis proposes three optimization-based control frameworks that are verified in simulation testbeds (with state-of-art simulation software and numerical models with MATLAB and Python). …

    mit Repository record for Simulation- and Experiment-Based Setpoint Control for Heating, Ventilation, and Air-Conditioning Systems: A Single- and Multi-Objective Optimization Problem (opens in a new tab)

  2. Optimization over networks: Efficient algorithms and analysis

    … domains can be formulated as a distributed convex constrained minimization problem over a multi-agent network. The problem is usually defined as a sum of convex objective functions over an intersection of convex constraint sets. The first part of this thesis is focused on the development and …

    uiuc Repository record for Optimization over networks: Efficient algorithms and analysis (opens in a new tab)

  3. Distributed model predictive control based consensus of general linear multi-agent systems with input constraints

    … under two different scenarios: (1) general constrained linear MASs with bounded additive disturbance; (2) linear MASs with input constraints underlying distributed communication networks. In Chapter 2, a tube-based robust MPC consensus protocol for constrained linear MASs is proposed. For …

    uvic Repository record for Distributed model predictive control based consensus of general linear multi-agent systems with input constraints (opens in a new tab)

  4. Optimization in Deep Learning: Structured, Realistic and Interpretable Learning for Decision-Making

    … adoption in high-stakes applications is often constrained by challenges related to interpretability, fairness, and generalization in structured or complex environments. This thesis develops new optimization methodologies to enhance the realism, structureawareness, and interpretability of deep …

    mit Repository record for Optimization in Deep Learning: Structured, Realistic and Interpretable Learning for Decision-Making (opens in a new tab)