Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 9 of 9 for “"Subgradient methods"”.
-
Subgradient methods for convex minimization
… problems, we have to employ special methods that can work in the absence of differentiability, while taking the advantage of convexity and possibly other special structures that our minimization problem may possess. In this thesis, we propose and analyze some new methods that can …
-
Distributed Newton-type algorithms for network resource allocation
… dual decomposition and first-order (gradient or subgradient) methods, which involve simple computations and can be implemented in a distributed manner, yet suffer from slow rate of convergence. Second-order methods are faster, but their direct implementation requires computation intensive matrix …
-
Quantum Advantage via Physics-Inspired Methods in Optimization: Empirical and Theoretical Analysis
… the precise conditions under which these methods provide a provable advantage over state-of-the-art classical algorithms are still largely unexplored. In this thesis, we make progress toward answering this question. First, we establish the global convergence of QHD for nonsmooth continuous …
-
Design and operation of electricity markets: dynamics, uncertainty, pricing and competition
… of the algorithm compares well with standard subgradient methods on the examples considered. Finally, the convex hull pricing scheme is compared with the currently employed marginal-cost pricing scheme in duopolistic power markets in which firms submit their offer functions while abiding by …
-
Distributed online algorithms for energy management in smart grids
… for distributed economic dispatch based on Subgradient method and Alternating Direction Method of Multipliers (ADMM), both designed to be agnostic with any initialization vector. The proposed distributed online solutions leverage a dynamic average consensus algorithm to track the …
-
Accelerated first-order optimization methods using inertia and error bounds
… applications. The focus is on first-order methods which have low per-iteration complexity and can exploit problem structure to a high degree. First-order methods have the capacity to address large-scale problems for which all alternative methods fail. However, first-order methods can take …
-
Tight Flow-Based Formulations for the Asymmetric Traveling Salesman Problem and Their Applications to some Scheduling Problems
… to (near-) optimality by using deflected subgradient methods on Lagrangian dual formulations. We solve the LP relaxation of our tightest formulation, ATSP6, to (near-) optimality by using a deflected subgradient algorithm with average direction strategy (SA_ADS) (see Sherali and Ulular …
-
Nondifferentiable Optimization of Lagrangian Dual Formulations for Linear Programs with Recovery of Primal Solutions
… which simplex as well as interior point based methods can tend to be ineffective. In contrast, Lagrangian relaxation or dual formulations, when applied in concert with suitable primal recovery strategies, have the potential for providing quick bounds as well as enabling useful branching …
-
Discrete Two-Stage Stochastic Mixed-Integer Programs with Applications to Airline Fleet Assignment and Workforce Planning Problems
… non-smooth Lagrangian dual problems using subgradient methods in the bounding process, which turns out to be computationally very expensive. We begin with proposing a decomposition-based branch-and-bound (DBAB) algorithm for solving two-stage stochastic programs having 0-1 mixed-integer …