389 research outputs found
A posteriori multi-stage optimal trading under transaction costs and a diversification constraint
This paper presents a simple method for a posteriori (historical)
multi-variate multi-stage optimal trading under transaction costs and a
diversification constraint. Starting from a given amount of money in some
currency, we analyze the stage-wise optimal allocation over a time horizon with
potential investments in multiple currencies and various assets. Three variants
are discussed, including unconstrained trading frequency, a fixed number of
total admissable trades, and the waiting of a specific time-period after every
executed trade until the next trade. The developed methods are based on
efficient graph generation and consequent graph search, and are evaluated
quantitatively on real-world data. The fundamental motivation of this work is
preparatory labeling of financial time-series data for supervised machine
learning.Comment: 25 pages, 4 figures, 6 table
A Convex Feasibility Approach to Anytime Model Predictive Control
This paper proposes to decouple performance optimization and enforcement of
asymptotic convergence in Model Predictive Control (MPC) so that convergence to
a given terminal set is achieved independently of how much performance is
optimized at each sampling step. By embedding an explicit decreasing condition
in the MPC constraints and thanks to a novel and very easy-to-implement convex
feasibility solver proposed in the paper, it is possible to run an outer
performance optimization algorithm on top of the feasibility solver and
optimize for an amount of time that depends on the available CPU resources
within the current sampling step (possibly going open-loop at a given sampling
step in the extreme case no resources are available) and still guarantee
convergence to the terminal set. While the MPC setup and the solver proposed in
the paper can deal with quite general classes of functions, we highlight the
synthesis method and show numerical results in case of linear MPC and
ellipsoidal and polyhedral terminal sets.Comment: 8 page
Direct data-driven control of constrained linear parameter-varying systems: A hierarchical approach
In many nonlinear control problems, the plant can be accurately described by
a linear model whose operating point depends on some measurable variables,
called scheduling signals. When such a linear parameter-varying (LPV) model of
the open-loop plant needs to be derived from a set of data, several issues
arise in terms of parameterization, estimation, and validation of the model
before designing the controller. Moreover, the way modeling errors affect the
closed-loop performance is still largely unknown in the LPV context. In this
paper, a direct data-driven control method is proposed to design LPV
controllers directly from data without deriving a model of the plant. The main
idea of the approach is to use a hierarchical control architecture, where the
inner controller is designed to match a simple and a-priori specified
closed-loop behavior. Then, an outer model predictive controller is synthesized
to handle input/output constraints and to enhance the performance of the inner
loop. The effectiveness of the approach is illustrated by means of a simulation
and an experimental example. Practical implementation issues are also
discussed.Comment: Preliminary version of the paper "Direct data-driven control of
constrained systems" published in the IEEE Transactions on Control Systems
Technolog
Forward-backward truncated Newton methods for convex composite optimization
This paper proposes two proximal Newton-CG methods for convex nonsmooth
optimization problems in composite form. The algorithms are based on a a
reformulation of the original nonsmooth problem as the unconstrained
minimization of a continuously differentiable function, namely the
forward-backward envelope (FBE). The first algorithm is based on a standard
line search strategy, whereas the second one combines the global efficiency
estimates of the corresponding first-order methods, while achieving fast
asymptotic convergence rates. Furthermore, they are computationally attractive
since each Newton iteration requires the approximate solution of a linear
system of usually small dimension
E.P. v. Alaska Psychiatric Institute: The Evolution of Involuntary Civil Commitments from Treatment to Punishment
Addresses the problem of identification of hybrid dynamical systems, by focusing the attention on hinging hyperplanes and Wiener piecewise affine autoregressive exogenous models. In particular, we provide algorithms based on mixed-integer linear or quadratic programming which are guaranteed to converge to a global optimu
Douglas-Rachford Splitting: Complexity Estimates and Accelerated Variants
We propose a new approach for analyzing convergence of the Douglas-Rachford
splitting method for solving convex composite optimization problems. The
approach is based on a continuously differentiable function, the
Douglas-Rachford Envelope (DRE), whose stationary points correspond to the
solutions of the original (possibly nonsmooth) problem. By proving the
equivalence between the Douglas-Rachford splitting method and a scaled gradient
method applied to the DRE, results from smooth unconstrained optimization are
employed to analyze convergence properties of DRS, to tune the method and to
derive an accelerated version of it
Recurrent Neural Network Training with Convex Loss and Regularization Functions by Extended Kalman Filtering
This paper investigates the use of extended Kalman filtering to train
recurrent neural networks with rather general convex loss functions and
regularization terms on the network parameters, including
-regularization. We show that the learning method is competitive with
respect to stochastic gradient descent in a nonlinear system identification
benchmark and in training a linear system with binary outputs. We also explore
the use of the algorithm in data-driven nonlinear model predictive control and
its relation with disturbance models for offset-free closed-loop tracking.Comment: 21 pages, 3 figures, submitted for publicatio
Active Learning for Regression by Inverse Distance Weighting
This paper proposes an active learning (AL) algorithm to solve regression
problems based on inverse-distance weighting functions for selecting the
feature vectors to query. The algorithm has the following features: (i)
supports both pool-based and population-based sampling; (ii) it is not tailored
to a particular class of predictors; (iii) can handle known and unknown
constraints on the queryable feature vectors; and (iv) can run either
sequentially, or in batch mode, depending on how often the predictor is
retrained. The potentials of the method are shown in numerical tests on
illustrative synthetic problems and real-world datasets from the UCI
repository. A Python implementation of the algorithm, that we call IDEAL
(Inverse-Distance based Exploration for Active Learning), is available at
\url{http://cse.lab.imtlucca.it/~bemporad/ideal}.Comment: 21 pages, 9 figures. Submitted for publicatio
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