Stochastic Optimization
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
... We performed experiments with several real world data sets with different characteristics: the Im- ageNet image database (Deng et al., 2009), the Reuters RCV1 text classification data set (Lewis et al., 2004), the MNIST ...
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Parallel algorithms for two-stage stochastic optimization
... transportation. Stochastic optimization has been used previously for planning in ...stage stochastic programming model to plan the transportation of first-aid commodities to disaster-affected ...a ...
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Probabilistic Line Searches for Stochastic Optimization
... deterministic optimization, line searches are a standard tool ensuring stability and ...only stochastic gradients are available, no direct equivalent has so far been formulated, because uncertain gradients ...
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Stochastic optimization model of aquacultured fish for sale and ecological education
... and stochastic [] optimization models to achieve cost-effective manage- ment of artificial aquaculture systems ...in optimization models of aqua- cultured fishes of FCs in Japan, in which fishes are ...
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SGDLibrary: A MATLAB library for stochastic optimization algorithms
... in the field of machine learning (ML). One promising approach for large-scale data is to use a stochastic optimization algorithm to solve the problem. SGDLibrary is a readable, flexible and extensible ...
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Approximation Algorithms for Distributionally Robust Stochastic Optimization
... classical stochastic model, to address the issue that in practice one typically does not have a probability distribution that precisely describes the behavior of the uncertain parame- ...classical ...
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A Framework for Analyzing Stochastic Optimization Algorithms Under Dependence
... the optimization of compositions of stochas- tic ...a stochastic gradient is already computationally ...biased stochastic gradi- ents in their algorithmic design, which results in non-optimal ...
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Stochastic Optimization approaches for trading on financial and energy markets
... The energy market is going through a period of transition. On the one hand the liberalization gave everyone the opportunity for an equal and fair access to the grid; small-size producers and end-users of electricity ...
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STOCHASTIC OPTIMIZATION IN MULTIVARIATE STRATIFIED DOUBLE SAMPLING DESIGN
... In this paper, a method of optimum allocation for multivariate stratified double sampling is developed. The problems of determining the optimum allocations are formulated as Nonlinear Programming problems (NLPP) in which ...
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Proper efficiency and tradeoffs in multiple criteria and stochastic optimization
... and stochastic or robust ...and stochastic programming have been shown to be special cases of certain other linear or generally nonlinear scalarization functions by Klamroth et ...
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Effectual Multiprocessor Scheduling Based on Stochastic Optimization Technique
... by the stochastic mechanism to escape from local optima. If c1=c2 each particle attracts to the average of pbest and gbest. Since c1 expresses how much particle trusts its own past experience it is called the ...
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Stochastic Optimization For Multi-Agent Statistical Learning And Control
... compositional stochastic program and we develop a functional extension of stochastic quasi-gradient algorithm operating in tandem with the greedy subspace projections mentioned ...
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Stochastic Optimization and Machine Learning Modeling for Wireless Networking
... As a result, the optimal policy is related to an efficient management of the energy scavenged from the environment, trying to avoid both energy outage ( i.e. , no energy available at the d[r] ...
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Stochastic Optimization for Nuclear Facility Deployment Scenarios.
... In the VISION simulation, each reactor type is assumed to run identical fuel cycling schemes with fixed fresh and spent fuel recipes. For each reactor, only the total fuel mass and fuel type are tracked, not the ...
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Stochastic Optimization of Energy Harvesting Wireless Communication Networks
... the stochastic optimal control algorithm which minimizes the expected energy downlink power and stabilizes the ...queues. Optimization over multiple slots was considered in an Orthogonal Frequency-Division ...
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Combining stochastic programming and optimal control to solve multistage stochastic optimization problems
... rely on Newton’s method or successive modifications of it. One of the issue which arise in the solution of this kind of system is related to the presence of nonnegativity constraints. Interior point methods introduce ...
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Stochastic Optimization for Big Data Analytics: Algorithms and Libraries
... Stochastic Gradient Descent (Pegasos) for L1-SVM (primal) Stochastic Dual Coordinate Ascent (SDCA) for L2-SVM (dual) Stochastic Average Gradient (SAG) for Logistic Regression/Regression?[r] ...
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Stochastic Dynamic Optimization Under Ambiguity
... of stochastic dynamic optimization describes mathematical tools that can be used to inform decision-making in these challenging ...Dynamic optimization describes the methods used when these decisions ...
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Two-Stage Stochastic Mixed Integer Linear Optimization
... of stochastic optimization not only because they provide proofs of optimality, but also they provide a natural way of performing sensitivity analyses, since such a function provides provable bounds on the ...
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Some Optimization Problems for Stochastic Systems with Memory.
... In this dissertation, we investigate some stochastic optimization models of Merton’s type with delays. In chapter 2, an optimal-investment consumption model over a finite time horizon with finite delay is ...
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