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Showing 1 to 4 of 4 for “"augmented Lagrangian approach"”.

  1. Traversing Rugged Domains: Explorations in Non-convex Optimization Theory and Software

    … Particle Swarm Optimization with L-BFGS, and an augmented Lagrangian approach with stochastic inner optimizers that connects constrained optimization with machine learning techniques. Our work combines theoretical foundations with practical implementation, providing researchers tools to use …

    mit Repository record for Traversing Rugged Domains: Explorations in Non-convex Optimization Theory and Software (opens in a new tab)

  2. Exterior Penalty Approaches for Solving Linear Programming Problems

    … effort, we study three exterior penalty function approaches for solving linear programming problems. These methods are an active set l2 penalty approach (ASL2), an inequality-equality based l2 penalty approach (IEL2), and an augmented Lagrangian approach (ALAG). Particular effective variants are …

    vt Repository record for Exterior Penalty Approaches for Solving Linear Programming Problems (opens in a new tab)

  3. Direct numerical simulation of viscoplastic particulate flows

    … specialised solution strategies. We adopt an augmented Lagrangian approach that allows for an exact treatment of the constitutive equation, enabling truly unyielded zones in our solutions. Even with an efficient discretisation scheme, the solution of viscoplatic fluid flow problems remains …

    cambridge Repository record for Direct numerical simulation of viscoplastic particulate flows (opens in a new tab)

  4. Algorithms for Sparse and Low-Rank Optimization: Convergence, Complexity and Applications

    … as a semidefinite programming problem, such an approach is computationally expensive when the matrices are large. In Chapter 2, we propose fixed-point and Bregman iterative algorithms for solving the nuclear norm minimization problem and prove convergence of the first of these algorithms. By …

    columbia-diss Repository record for Algorithms for Sparse and Low-Rank Optimization: Convergence, Complexity and Applications (opens in a new tab)