81 research outputs found
Necessary and sufficient condition on global optimality without convexity and second order differentiability
The main goal of this paper is to give a necessary and sufficient condition
of global optimality for unconstrained optimization problems, when the objective
function is not necessarily convex. We use Gâteaux differentiability of the objective
function and its bidual (the latter is known from convex analysis)
Mirror-Descent Methods in Mixed-Integer Convex Optimization
In this paper, we address the problem of minimizing a convex function f over
a convex set, with the extra constraint that some variables must be integer.
This problem, even when f is a piecewise linear function, is NP-hard. We study
an algorithmic approach to this problem, postponing its hardness to the
realization of an oracle. If this oracle can be realized in polynomial time,
then the problem can be solved in polynomial time as well. For problems with
two integer variables, we show that the oracle can be implemented efficiently,
that is, in O(ln(B)) approximate minimizations of f over the continuous
variables, where B is a known bound on the absolute value of the integer
variables.Our algorithm can be adapted to find the second best point of a
purely integer convex optimization problem in two dimensions, and more
generally its k-th best point. This observation allows us to formulate a
finite-time algorithm for mixed-integer convex optimization
Templates for Convex Cone Problems with Applications to Sparse Signal Recovery
This paper develops a general framework for solving a variety of convex cone
problems that frequently arise in signal processing, machine learning,
statistics, and other fields. The approach works as follows: first, determine a
conic formulation of the problem; second, determine its dual; third, apply
smoothing; and fourth, solve using an optimal first-order method. A merit of
this approach is its flexibility: for example, all compressed sensing problems
can be solved via this approach. These include models with objective
functionals such as the total-variation norm, ||Wx||_1 where W is arbitrary, or
a combination thereof. In addition, the paper also introduces a number of
technical contributions such as a novel continuation scheme, a novel approach
for controlling the step size, and some new results showing that the smooth and
unsmoothed problems are sometimes formally equivalent. Combined with our
framework, these lead to novel, stable and computationally efficient
algorithms. For instance, our general implementation is competitive with
state-of-the-art methods for solving intensively studied problems such as the
LASSO. Further, numerical experiments show that one can solve the Dantzig
selector problem, for which no efficient large-scale solvers exist, in a few
hundred iterations. Finally, the paper is accompanied with a software release.
This software is not a single, monolithic solver; rather, it is a suite of
programs and routines designed to serve as building blocks for constructing
complete algorithms.Comment: The TFOCS software is available at http://tfocs.stanford.edu This
version has updated reference
Oracle-based optimization applied to climate model calibration
In this paper, we show how oracle-based optimization can be effectively used for the calibration of an intermediate complexity climate model. In a fully developed example, we estimate the 12 principal parameters of the C-GOLDSTEIN climate model by using an oracle- based optimization tool, Proximal-ACCPM. The oracle is a procedure that finds, for each query point, a value for the goodness-of-fit function and an evaluation of its gradient. The difficulty in the model calibration problem stems from the need to undertake costly calculations for each simulation and also from the fact that the error function used to assess the goodness-of-fit is not convex. The method converges to a Fbest fit_ estimate over 10 times faster than a comparable test using the ensemble Kalman filter. The approach is simple to implement and potentially useful in calibrating computationally demanding models based on temporal integration (simulation), for which functional derivative information is not readily available
Lyapunov spectrum of asymptotically sub-additive potentials
For general asymptotically sub-additive potentials (resp. asymptotically
additive potentials) on general topological dynamical systems, we establish
some variational relations between the topological entropy of the level sets of
Lyapunov exponents, measure-theoretic entropies and topological pressures in
this general situation. Most of our results are obtained without the assumption
of the existence of unique equilibrium measures or the differentiability of
pressure functions. Some examples are constructed to illustrate the
irregularity and the complexity of multifractal behaviors in the sub-additive
case and in the case that the entropy map that is not upper-semi continuous.Comment: 44 page
Differential inequalities on complete Riemannian manifolds and applications
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46226/1/208_2005_Article_BF01455859.pd
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