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Showing 1 to 20 of 46 for “"stochastic approximation"”.
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On stochastic approximation
… is the desired level of response. A stochastic approximation is a sequential estimation procedure where future observation points are determined on the basis of past information. The two most-discussed procedures for the problem described are the Robbins-Monro (R-M) procedure and the …
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Consensus-type stochastic approximation algorithms
… The consensus control is achieved by designing stochastic approximation algorithms. In the setup, the regime-switching process (the Markov chain) contains a rate parameter</p> <p>"Ε> 0 in the transition probability matrix that characterizes how frequently the topology switches. On the other …
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Specific optimal control using stochastic approximation
… initial state to be a random variable. Then the "stochastic optimal controller" is defined as the set of constant parameters which minimizes the expected value with respect to the initial state of a suitably chosen performance index. The method of stochastic approximation is proposed as a means of …
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Dynamic multi-ramp metering control with simultaneous perturbation stochastic approximation (SPSA)
… were determined. A Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm is proposed to dynamically optimize multiple-ramp metering control by maximizing the total throughput subject to a number of constraints. The ramp metering rates subject to dynamic traffic conditions and …
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Stochastic approximation for non-expansive maps : application to Q-learning algorithms
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
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Stochastic Approximation Algorithms With Applications To Particle Swarm Optimization, Adaptive Optimization, And Consensus
… three problems arising in recent applications of stochastic approximation methods. In Chapter 2, we use stochastic approximation to analyze Particle Swarm Optimization (PSO) algorithm. We introduce four coefficients and rewrite the PSO procedure as a stochastic approximation type iterative …
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Stochastic approximation schemes for stochastic optimization and variational problems: adaptive steplengths, smoothing, and regularization
Stochastic approximation (SA) methods, first proposed by Robbins and Monro in 1951 for root- finding problems, have been widely used in the literature to solve problems arising from stochastic convex optimization, stochastic Nash games and more recently stochastic variational inequalities. Several …
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Foundations of Multiple-Time-Scale Stochastic Approximation for Fast and Resilient Distributed Optimization Algorithms
… the impact of both malicious agents and stochastic gradients, while still ensuring exact convergence at the optimal rate. Finally, we study open networks, where agents can dynamically join or leave. By leveraging redundancy in local objectives, we convert the time-varying problem into an …
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W-SPSA : an Efficient Stochastic Approximation Algorithm for the off-line calibration of Dynamic Traffic Assignment models
… optimization problem. Simultaneous Perturbation Stochastic Approximation (SPSA) has been reported in the literature to be the most suitable solution algorithm for this problem due to its highly efficient gradient estimation approach. However, it turns out that the performance of SPSA in terms of …
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Improving algorithmic performance using stochastic learning rates: In-expectation and almost-surely stochastic approximation, and online learning, results and applications
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Approximating the Heston-Hull-White Model
… (HHW) model combines the Heston (1993) stochastic volatility and Hull and White (1990) short rate models. Compared to stochastic volatility models, hybrid models improve upon the pricing and hedging of longdated options and equity-interest rate hybrid claims. When the Heston and …
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Stochastic optimization algorithms for adaptive modulation in software defined radio
… into mobile devices. In this thesis we present stochastic learning algorithms for adaptive modulation design. The algorithms presented allow for adaptive modulation system design in-dependent of error correction coding and modulation constellation requirements. In real time, the performance of …
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Numerical stability of Monte Carlo neutron transport and isotopic depletion for nuclear reactor analysis
… coupling previously in the form of the stochastic approximation, although this is relatively inefficient. The relaxation scheme proposed here uses a fixed relaxation factor (rather than the variable factor previously used) and demonstrates its improved stability and computational …
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Model reduction of Markov chains with applications to building systems
… policies is introduced. This makes possible a stochastic approximation approach to compute the best policy within a given parameterized class. Convergence of stochastic approximation approach is established using the ODE method. The aggregation-based model reduction method of Markov chains is …
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Sampling Controlled Stochastic Recursions: Applications to Simulation Optimization and Stochastic Root Finding
… point by repeatedly executing a Monte Carlo (stochastic) simulation. SO, introduced more than six decades ago through the seminal work of Robbins and Monro (and later by Kiefer and Wolfowitz), has recently generated much attention. Such interest is primarily because of SOs flexibility, …
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Variational Approximation for Complex Regression Models
… for more flexible models and fast computational approximations. My thesis reflects these themes by considering some very flexible regression models and developing fast variational approximation methods for fitting them under a Bayesian framework. Models considered include mixtures of …
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Stochastic and deterministic multipatch epidemic models
… thresholds for both the deterministic and the stochastic system through an approximating multi-type branching process. We formulate a continuous time Markov Chain that corresponds to each deterministic system studied. The main idea will be to study the spectral radius of the next generation …
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Optimal Bayesian experimental design in the presence of model error
… to evaluate the expected information gain, and stochastic approximation algorithms make optimization feasible for computationally intensive and high-dimensional problems. A key aspect of our framework is the introduction of model calibration discrepancy terms that are used to "relax" the model …
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Essays on the economics of racial segregation
… of nonlinear equations, which I solve using a stochastic approximation algorithm. To perform the stochastic approximation, I develop an algorithm to generate samples from the stationary equilibrium of the model. The algorithm is a variant of the Simulated tempering and allows fast convergence …
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Distributed algorithms for networked multi-agent systems: optimization and competition
… of distributed gradient-based algorithms on an approximation of the multiuser problem. Such an approximation is obtained through a regularization and is equipped with bounds of the difference between the optimal function values of the original problem and its regularized counterpart. In the …
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