829 research outputs found
Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems
We propose a trust-region stochastic sequential quadratic programming
algorithm (TR-StoSQP) to solve nonlinear optimization problems with stochastic
objectives and deterministic equality constraints. We consider a fully
stochastic setting, where at each step a single sample is generated to estimate
the objective gradient. The algorithm adaptively selects the trust-region
radius and, compared to the existing line-search StoSQP schemes, allows us to
utilize indefinite Hessian matrices (i.e., Hessians without modification) in
SQP subproblems. As a trust-region method for constrained optimization, our
algorithm must address an infeasibility issue -- the linearized equality
constraints and trust-region constraints may lead to infeasible SQP
subproblems. In this regard, we propose an adaptive relaxation technique to
compute the trial step, consisting of a normal step and a tangential step. To
control the lengths of these two steps while ensuring a scale-invariant
property, we adaptively decompose the trust-region radius into two segments,
based on the proportions of the rescaled feasibility and optimality residuals
to the rescaled full KKT residual. The normal step has a closed form, while the
tangential step is obtained by solving a trust-region subproblem, to which a
solution ensuring the Cauchy reduction is sufficient for our study. We
establish a global almost sure convergence guarantee for TR-StoSQP, and
illustrate its empirical performance on both a subset of problems in the CUTEst
test set and constrained logistic regression problems using data from the
LIBSVM collection.Comment: 10 figures, 33 page
Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching
We consider solving equality-constrained nonlinear, nonconvex optimization
problems. This class of problems appears widely in a variety of applications in
machine learning and engineering, ranging from constrained deep neural
networks, to optimal control, to PDE-constrained optimization. We develop an
adaptive inexact Newton method for this problem class. In each iteration, we
solve the Lagrangian Newton system inexactly via a randomized iterative
sketching solver, and select a suitable stepsize by performing line search on
an exact augmented Lagrangian merit function. The randomized solvers have
advantages over deterministic linear system solvers by significantly reducing
per-iteration flops complexity and storage cost, when equipped with suitable
sketching matrices. Our method adaptively controls the accuracy of the
randomized solver and the penalty parameters of the exact augmented Lagrangian,
to ensure that the inexact Newton direction is a descent direction of the exact
augmented Lagrangian. This allows us to establish a global almost sure
convergence. We also show that a unit stepsize is admissible locally, so that
our method exhibits a local linear convergence. Furthermore, we prove that the
linear convergence can be strengthened to superlinear convergence if we
gradually sharpen the adaptive accuracy condition on the randomized solver. We
demonstrate the superior performance of our method on benchmark nonlinear
problems in CUTEst test set, constrained logistic regression with data from
LIBSVM, and a PDE-constrained problem.Comment: 25 pages, 4 figure
A Modified Direct Power Control Strategy Allowing the Connection of Three-Phase Inverters to the Grid Through LCL Filters
Abstract — This paper proposes a novel approach to adapt the conventional Direct Power Control (DPC) for high power applications with a third order LCL filter. The strong resonance present in the LCL filter is damped with additional effort in the system control. The application of DPC to the control of threephase Voltage Source Inverter (VSI) connected to the grid through a LCL filter has not yet been considered. An active damping strategy for the LCL filter together with harmonic rejection control is proposed over the conventional DPC. The steady state as well as the dynamic performance of the proposed system is presented by means of the simulation results and compared with the conventional approach
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Novel bidirectional universal 1-phase/3-phase-input unity power factor differential AC/DC converter
A common 400 V dc bus for industrial motor drives advantageously allows the use of high-performance 600 V power semiconductor technology in the inverter drive converter stages and to lower the rated power of the supplying rectifier system. Ideally, this supplying rectifier system features unity power factor operation, bidirectional power flow and nominal power operation in the three-phase and the single-phase grid. This paper introduces a novel bidirectional universal single-/three-phase-input unity power factor differential ac-dc converter suitable for the above mentioned requirements. The basic operating principle and conduction states of the proposed topology are derived and discussed in detail. Then, the main power component voltage and current stresses are determined and simulation results in PLECS are provided. The concept is verified by means of experimental measurements conducted in both three-phase and single-phase operation with a 6 kW prototype system employing a switching frequency of 100 kHz and 1200 V SiC power semiconductor
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