837 research outputs found
Some Applications of Polynomial Optimization in Operations Research and Real-Time Decision Making
We demonstrate applications of algebraic techniques that optimize and certify
polynomial inequalities to problems of interest in the operations research and
transportation engineering communities. Three problems are considered: (i)
wireless coverage of targeted geographical regions with guaranteed signal
quality and minimum transmission power, (ii) computing real-time certificates
of collision avoidance for a simple model of an unmanned vehicle (UV)
navigating through a cluttered environment, and (iii) designing a nonlinear
hovering controller for a quadrotor UV, which has recently been used for load
transportation. On our smaller-scale applications, we apply the sum of squares
(SOS) relaxation and solve the underlying problems with semidefinite
programming. On the larger-scale or real-time applications, we use our recently
introduced "SDSOS Optimization" techniques which result in second order cone
programs. To the best of our knowledge, this is the first study of real-time
applications of sum of squares techniques in optimization and control. No
knowledge in dynamics and control is assumed from the reader
A generalization of the integer linear infeasibility problem
Does a given system of linear equations with nonnegative constraints have an
integer solution? This is a fundamental question in many areas. In statistics
this problem arises in data security problems for contingency table data and
also is closely related to non-squarefree elements of Markov bases for sampling
contingency tables with given marginals. To study a family of systems with no
integer solution, we focus on a commutative semigroup generated by a finite
subset of and its saturation. An element in the difference of the
semigroup and its saturation is called a ``hole''. We show the necessary and
sufficient conditions for the finiteness of the set of holes. Also we define
fundamental holes and saturation points of a commutative semigroup. Then, we
show the simultaneous finiteness of the set of holes, the set of non-saturation
points, and the set of generators for saturation points. We apply our results
to some three- and four-way contingency tables. Then we will discuss the time
complexities of our algorithms.Comment: This paper has been published in Discrete Optimization, Volume 5,
Issue 1 (2008) p36-5
Sequential Convex Programming Methods for Solving Nonlinear Optimization Problems with DC constraints
This paper investigates the relation between sequential convex programming
(SCP) as, e.g., defined in [24] and DC (difference of two convex functions)
programming. We first present an SCP algorithm for solving nonlinear
optimization problems with DC constraints and prove its convergence. Then we
combine the proposed algorithm with a relaxation technique to handle
inconsistent linearizations. Numerical tests are performed to investigate the
behaviour of the class of algorithms.Comment: 18 pages, 1 figur
Inverse Optimization: Closed-form Solutions, Geometry and Goodness of fit
In classical inverse linear optimization, one assumes a given solution is a
candidate to be optimal. Real data is imperfect and noisy, so there is no
guarantee this assumption is satisfied. Inspired by regression, this paper
presents a unified framework for cost function estimation in linear
optimization comprising a general inverse optimization model and a
corresponding goodness-of-fit metric. Although our inverse optimization model
is nonconvex, we derive a closed-form solution and present the geometric
intuition. Our goodness-of-fit metric, , the coefficient of
complementarity, has similar properties to from regression and is
quasiconvex in the input data, leading to an intuitive geometric
interpretation. While is computable in polynomial-time, we derive a
lower bound that possesses the same properties, is tight for several important
model variations, and is even easier to compute. We demonstrate the application
of our framework for model estimation and evaluation in production planning and
cancer therapy
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