10 research outputs found
Confidence bands for a log-concave density
We present a new approach for inference about a log-concave distribution:
Instead of using the method of maximum likelihood, we propose to incorporate
the log-concavity constraint in an appropriate nonparametric confidence set for
the cdf . This approach has the advantage that it automatically provides a
measure of statistical uncertainty and it thus overcomes a marked limitation of
the maximum likelihood estimate. In particular, we show how to construct
confidence bands for the density that have a finite sample guaranteed
confidence level. The nonparametric confidence set for which we introduce
here has attractive computational and statistical properties: It allows to
bring modern tools from optimization to bear on this problem via difference of
convex programming, and it results in optimal statistical inference. We show
that the width of the resulting confidence bands converges at nearly the
parametric rate when the log density is -affine.Comment: Added more experiments, other minor change
Energy Efficiency Maximization for C-RANs: Discrete Monotonic Optimization, Penalty, and l0-Approximation Methods
We study downlink of multiantenna cloud radio access networks (C-RANs) with
finite-capacity fronthaul links. The aim is to propose joint designs of
beamforming and remote radio head (RRH)-user association, subject to
constraints on users' quality-of-service, limited capacity of fronthaul links
and transmit power, to maximize the system energy efficiency. To cope with the
limited-capacity fronthaul we consider the problem of RRH-user association to
select a subset of users that can be served by each RRH. Moreover, different to
the conventional power consumption models, we take into account the dependence
of baseband signal processing power on the data rate, as well as the dynamics
of the efficiency of power amplifiers. The considered problem leads to a mixed
binary integer program (MBIP) which is difficult to solve. Our first
contribution is to derive a globally optimal solution for the considered
problem by customizing a discrete branch-reduce-and-bound (DBRB) approach.
Since the global optimization method requires a high computational effort, we
further propose two suboptimal solutions able to achieve the near optimal
performance but with much reduced complexity. To this end, we transform the
design problem into continuous (but inherently nonconvex) programs by two
approaches: penalty and \ell_{0}-approximation methods. These resulting
continuous nonconvex problems are then solved by the successive convex
approximation framework. Numerical results are provided to evaluate the
effectiveness of the proposed approaches.Comment: IEEE Transaction on Signal Processing, September 2018 (15 pages, 12
figures
Partial Identification with Proxy of Latent Confoundings via Sum-of-ratios Fractional Programming
Due to the unobservability of confoundings, there has been widespread concern
about how to compute causality quantitatively. To address this challenge,
proxy-based negative control approaches have been commonly adopted, where
auxiliary outcome variables are introduced as the proxy of
confoundings . However, these approaches rely on strong assumptions
such as reversibility, completeness, or bridge functions. These assumptions
lack intuitive empirical interpretation and solid verification techniques,
hence their applications in the real world are limited. For instance, these
approaches are inapplicable when the transition matrix
is irreversible. In this paper, we focus on a weaker assumption called the
partial observability of . We develop a more general
single-proxy negative control method called Partial Identification via
Sum-of-ratios Fractional Programming (PI-SFP). It is a global optimization
algorithm based on the branch-and-bound strategy, aiming to provide the valid
bound of the causal effect. In the simulation, PI-SFP provides promising
numerical results and fills in the blank spots that can not be handled in the
previous literature, such as we have partial information of
Adaptive exact penalty DC algorithms for nonsmooth DC optimization problems with equality and inequality constraints
We propose and study two DC (difference of convex functions) algorithms based
on exact penalty functions for solving nonsmooth DC optimization problems with
nonsmooth DC equality and inequality constraints. Both methods employ adaptive
penalty updating strategies to improve their performance. The first method is
based on exact penalty functions with individual penalty parameter for each
constraint (i.e. multidimensional penalty parameter) and utilizes a primal-dual
approach to penalty updates. The second method is based on the so-called
steering exact penalty methodology and relies on solving some auxiliary convex
subproblems to determine a suitable value of the penalty parameter. We present
a detailed convergence analysis of both methods and give several simple
numerical examples highlighting peculiarites of two different penalty updating
strategies studied in this paper
DC Semidefinite Programming and Cone Constrained DC Optimization
In the first part of this paper we discuss possible extensions of the main
ideas and results of constrained DC optimization to the case of nonlinear
semidefinite programming problems (i.e. problems with matrix constraints). To
this end, we analyse two different approaches to the definition of DC
matrix-valued functions (namely, order-theoretic and componentwise), study some
properties of convex and DC matrix-valued functions and demonstrate how to
compute DC decompositions of some nonlinear semidefinite constraints appearing
in applications. We also compute a DC decomposition of the maximal eigenvalue
of a DC matrix-valued function, which can be used to reformulate DC
semidefinite constraints as DC inequality constrains.
In the second part of the paper, we develop a general theory of cone
constrained DC optimization problems. Namely, we obtain local optimality
conditions for such problems and study an extension of the DC algorithm (the
convex-concave procedure) to the case of general cone constrained DC
optimization problems. We analyse a global convergence of this method and
present a detailed study of a version of the DCA utilising exact penalty
functions. In particular, we provide two types of sufficient conditions for the
convergence of this method to a feasible and critical point of a cone
constrained DC optimization problem from an infeasible starting point
DC Programming and DCA for General DC Programs
International audienc
Advances in knowledge discovery and data mining Part II
19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p