Sufficient Conditions for Feasibility and Optimality of Real-Time Optimization Schemes - II. Implementation Issues
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Huge scale machine learning problems are nowadays tackled by distributed optimization algorithms, i.e. algorithms that leverage the compute power of many devices for training. The communication overhead is a key bottleneck that hinders perfect scalability. ...
The research community has been making significant progress in hardware implementation, numerical computing and algorithm development for optimization-based control. However, there are two key challenges that still have to be overcome for optimization-base ...
This paper addresses the steady-state optimization of continuous processes in the presence of uncertainty in the form of unknown or time-varying model parameters, structural plant-model mismatch, and disturbances. To address these issues, we assume that ce ...
Many factors influence learners' performance on an activity beyond the knowledge required. Learners' on-task effort has been acknowledged for strongly relating to their educational outcomes, reflecting how actively they are engaged in that activity. Howeve ...
Iterative real-time optimization schemes that employ modifier adaptation add bias and gradient correction terms to the model that is used for optimization. These affine corrections lead to meeting the first-order necessary conditions of optimality of the p ...
The concept of reaction variants and invariants for lumped reaction systems has been known for several decades. Its applications encompass model identification, data reconciliation, state estimation and control using kinetic models. In this thesis, the con ...
Intelligent tutoring systems adapt the curriculum to the needs of the individual student. Therefore, an accurate representation and prediction of student knowledge is essential. Bayesian Knowledge Tracing (BKT) is a popular approach for student modeling. T ...
Real-time control systems (RTCSs) tolerate delay and crash faults by replicating the controller. Each replica computes and issues setpoints to actuators over a network that might drop or delay messages. Hence, the actuators might receive an inconsistent se ...
We consider the minimization of a function defined on a Riemannian manifold M accessible only through unbiased estimates of its gradients. We develop a geometric framework to transform a sequence of slowly converging iterates generated from stochastic gradi ...
This paper presents a first implementation of gradient, divergence, and particle tracing schemes for the EMC3 code, a stochastic 3D plasma fluid code widely employed for edge plasma and impurity transport modeling in tokamaks and stellarators. These scheme ...