31,230 research outputs found
Imposing Economic Constraints in Nonparametric Regression: Survey, Implementation and Extension
Economic conditions such as convexity, homogeneity, homotheticity, and monotonicity are all important assumptions or consequences of assumptions of economic functionals to be estimated. Recent research has seen a renewed interest in imposing constraints in nonparametric regression. We survey the available methods in the literature, discuss the challenges that present themselves when empirically implementing these methods and extend an existing method to handle general nonlinear constraints. A heuristic discussion on the empirical implementation for methods that use sequential quadratic programming is provided for the reader and simulated and empirical evidence on the distinction between constrained and unconstrained nonparametric regression surfaces is covered.identification, concavity, Hessian, constraint weighted bootstrapping, earnings function
A simple duality proof in convex quadratic programming with a quadratic constraint, and some applications
Cataloged from PDF version of article.In this paper a simple derivation of duality is presented for convex quadratic programs with a convex quadratic
constraint. This problem arises in a number of applications including trust region subproblems of nonlinear programming,
regularized solution of ill-posed least squares problems, and ridge regression problems in statistical analysis.
In general, the dual problem is a concave maximization problem with a linear equality constraint. We apply the duality
result to: (1) the trust region subproblem, (2) the smoothing of empirical functions, and (3) to piecewise quadratic trust
region subproblems arising in nonlinear robust Huber M-estimation problems in statistics. The results are obtained
from a straightforward application of Lagrange duality. Ó 2000 Elsevier Science B.V. All rights reserved
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
Learning-based control algorithms require data collection with abundant
supervision for training. Safe exploration algorithms ensure the safety of this
data collection process even when only partial knowledge is available. We
present a new approach for optimal motion planning with safe exploration that
integrates chance-constrained stochastic optimal control with dynamics learning
and feedback control. We derive an iterative convex optimization algorithm that
solves an \underline{Info}rmation-cost \underline{S}tochastic
\underline{N}onlinear \underline{O}ptimal \underline{C}ontrol problem
(Info-SNOC). The optimization objective encodes both optimal performance and
exploration for learning, and the safety is incorporated as distributionally
robust chance constraints. The dynamics are predicted from a robust regression
model that is learned from data. The Info-SNOC algorithm is used to compute a
sub-optimal pool of safe motion plans that aid in exploration for learning
unknown residual dynamics under safety constraints. A stable feedback
controller is used to execute the motion plan and collect data for model
learning. We prove the safety of rollout from our exploration method and
reduction in uncertainty over epochs, thereby guaranteeing the consistency of
our learning method. We validate the effectiveness of Info-SNOC by designing
and implementing a pool of safe trajectories for a planar robot. We demonstrate
that our approach has higher success rate in ensuring safety when compared to a
deterministic trajectory optimization approach.Comment: Submitted to RA-L 2020, review-
SOCP relaxation bounds for the optimal subset selection problem applied to robust linear regression
This paper deals with the problem of finding the globally optimal subset of h
elements from a larger set of n elements in d space dimensions so as to
minimize a quadratic criterion, with an special emphasis on applications to
computing the Least Trimmed Squares Estimator (LTSE) for robust regression. The
computation of the LTSE is a challenging subset selection problem involving a
nonlinear program with continuous and binary variables, linked in a highly
nonlinear fashion. The selection of a globally optimal subset using the branch
and bound (BB) algorithm is limited to problems in very low dimension,
tipically d<5, as the complexity of the problem increases exponentially with d.
We introduce a bold pruning strategy in the BB algorithm that results in a
significant reduction in computing time, at the price of a negligeable accuracy
lost. The novelty of our algorithm is that the bounds at nodes of the BB tree
come from pseudo-convexifications derived using a linearization technique with
approximate bounds for the nonlinear terms. The approximate bounds are computed
solving an auxiliary semidefinite optimization problem. We show through a
computational study that our algorithm performs well in a wide set of the most
difficult instances of the LTSE problem.Comment: 12 pages, 3 figures, 2 table
A Simple Iterative Algorithm for Parsimonious Binary Kernel Fisher Discrimination
By applying recent results in optimization theory variously known as optimization transfer or majorize/minimize algorithms, an algorithm for binary, kernel, Fisher discriminant analysis is introduced that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution. The problem is converted into a smooth optimization that can be solved iteratively with no greater overhead than iteratively re-weighted least-squares. The result is simple, easily programmed and is shown to perform, in terms of both accuracy and parsimony, as well as or better than a number of leading machine learning algorithms on two well-studied and substantial benchmarks
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