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.
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Showing 1 to 20 of 41 for “"subgradient"”.
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Subgradient methods for convex minimization
Many optimization problems arising in various applications require minimization of an objective cost function that is convex but not differentiable. Such a minimization arises, for example, in model construction, system identification, neural networks, pattern classification, and various …
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Recovery of primal solution in dual subgradient schemes
… problems. In particular, we employ the subgradient method to solve the Lagrangian dual of a convex constrained problem, and use a primal-averaging scheme to obtain near-optimal and near-feasible primal solutions. We numerically evaluate the performance of the scheme in the framework of …
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LP-based subgradient algorithm for joint pricing and inventory control problems
It is important for companies to manage their revenues and -reduce their costs efficiently. These goals can be achieved through effective pricing and inventory control strategies. This thesis studies a joint multi-period pricing and inventory control problem for a make-to-stock manufacturing …
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A primal-dual conjugate subgradient algorithm for large- scale/specially structured linear programming problems
… dissertation deals with a primal-dual conjugate subgradient-based algorithm for solving large-scale and/or specially structured linear programming problems. The proposed algorithm coordinates a Lagrangian dual function and a primal penalty function which satisfies a flexible set of specified …
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Optimization of multistage systems with nondifferentiable objective functions.
… the objective function is accounted for by using subgradient information. The objective of the subproblems generated consists of successive piecewise linear approximations of the stagewise objective function and the value function. In the parallel algorithm, an incentive coordination method is …
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Special versus standard algorithms for large-scale harvest scheduling problems
… in the final iterations. A Primal-Dual Conjugate Subgradient Algorithm is also coded and tuned to solve general Model II problems. Results show that the computational effort is greatly affected by the number of side constraints. If the number of side constraints is restricted, the Primal-Dual …
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Lagrangian Relaxation / Dual Approaches For Solving Large-Scale Linear Programming Problems
… motivation, we present a practical primal-dual subgradient algorithm that incorporates a dual ascent, a primal recovery, and a penalty function approach to recover a near optimal and feasible pair of primal and dual solutions. The proposed primal-dual approach is comprised of three stages. Stage …
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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 …
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Algorithmic Approaches for Solving the Euclidean Distance Location and Location-Allocation Problems
… for solving EMFLP, conjugate or deflected subgradient based algorithms along with suitable line-search strategies are proposed. The subgradient deflection method considered is the Average Direction Strategy (ADS) imbedded within the Variable Target Value Method (VTVM). The generation of two …
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Random projection methods for stochastic convex minimization
… The problem has random features. Gradient or subgradient of objective function carries stochastic errors. Number of constraint sets can be extensive or infinitely many. Constraint sets might not be known apriori yet revealed through random realizations or randomly chosen from a collection of …
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Distributed optimization with applications to sensor networks and machine learning
… like communication noise, stochastic subgradient errors and stochastic communication topologies. This enables our algorithms to be useful in a wide class of application areas in sensor networks and machine learning. Specifically the consideration of stochastic subgradient errors enable …
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Optimal configuration of digital communication network
… problem. The Lagrangian relaxation method and subgradient optimization procedure have been used to find reasonably good feasible solutions. Although the reliability for computer communication networks is as important as the cost factor, only the cost factor is considered in the context of this …
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Efficient design of reconfigurable intelligent surface assisted multi-group multicast beamforming
… allows us to employ a fast first-order projected subgradient algorithm (PSA) to solve the RIS MMF subproblem. Furthermore, we show that QoS problem and MMF problem in our RIS-aided scenario are inverse problems, and by using the optimal beamforming structure for BS beamformer, we propose a low …
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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 …
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Adams inequalities with exact growth condition : on Rn and the Heisenberg group
… when [alpha] is an even integer, and for the subgradient [del] H[subscript n].
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Fast distributed first-order methods
… superior than the rates of existing gradient or subgradient algorithms, and is confirmed by simulation results.
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A discrete equal-capacity p-Median problem
… of a reformulated problem via a conjugate subgradient optimization procedure. We obtain Benders’ cuts from the above procedures and proceed to a modified Benders’ approach to solve the continuous relaxation of the original problem. Finally a branch-and-bound algorithm that enumerates over …
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Nondifferentiable optimization algorithms with application to solving Lagrangian dual problems
… with this new step-length rule, we present a new subgradient deflection strategy in which a selected subgradient is rotated optimally toward a point that has an objective function value less than the incumbent target value. We also develop another deflection strategy based on Shor’s space dilation …
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Optimisation models and algorithms for multicast message routing and power control in wireless multihop networks
… coupled structure of the problem. We implement a subgradient method for solving the dual problem, and then look at ways to accelerate its convergence. We also investigate the behaviour and convergence of a simple but effective primal co-ordinate descent method before numerically investigating its …
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Distributed optimization on a wireless sensor network testbed
… tings are introduced, focusing on an incremental subgradient-based algorithm and a broadcast, gossip-based algorithm. These algorithms are applied to lo- calize a light source. This localization problem is formulated as a distributed optimization problem in which the global optimum is the true …
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