98,450 research outputs found
Exact Algorithms for 0-1 Integer Programs with Linear Equality Constraints
In this paper, we show -time and -space exact
algorithms for 0-1 integer programs where constraints are linear equalities and
coefficients are arbitrary real numbers. Our algorithms are quadratically
faster than exhaustive search and almost quadratically faster than an algorithm
for an inequality version of the problem by Impagliazzo, Lovett, Paturi and
Schneider (arXiv:1401.5512), which motivated our work. Rather than improving
the time and space complexity, we advance to a simple direction as inclusion of
many NP-hard problems in terms of exact exponential algorithms. Specifically,
we extend our algorithms to linear optimization problems
Towards parallelizable sampling-based Nonlinear Model Predictive Control
This paper proposes a new sampling-based nonlinear model predictive control
(MPC) algorithm, with a bound on complexity quadratic in the prediction horizon
N and linear in the number of samples. The idea of the proposed algorithm is to
use the sequence of predicted inputs from the previous time step as a warm
start, and to iteratively update this sequence by changing its elements one by
one, starting from the last predicted input and ending with the first predicted
input. This strategy, which resembles the dynamic programming principle, allows
for parallelization up to a certain level and yields a suboptimal nonlinear MPC
algorithm with guaranteed recursive feasibility, stability and improved cost
function at every iteration, which is suitable for real-time implementation.
The complexity of the algorithm per each time step in the prediction horizon
depends only on the horizon, the number of samples and parallel threads, and it
is independent of the measured system state. Comparisons with the fmincon
nonlinear optimization solver on benchmark examples indicate that as the
simulation time progresses, the proposed algorithm converges rapidly to the
"optimal" solution, even when using a small number of samples.Comment: 9 pages, 9 pictures, submitted to IFAC World Congress 201
A new Lenstra-type Algorithm for Quasiconvex Polynomial Integer Minimization with Complexity 2^O(n log n)
We study the integer minimization of a quasiconvex polynomial with
quasiconvex polynomial constraints. We propose a new algorithm that is an
improvement upon the best known algorithm due to Heinz (Journal of Complexity,
2005). This improvement is achieved by applying a new modern Lenstra-type
algorithm, finding optimal ellipsoid roundings, and considering sparse
encodings of polynomials. For the bounded case, our algorithm attains a
time-complexity of s (r l M d)^{O(1)} 2^{2n log_2(n) + O(n)} when M is a bound
on the number of monomials in each polynomial and r is the binary encoding
length of a bound on the feasible region. In the general case, s l^{O(1)}
d^{O(n)} 2^{2n log_2(n) +O(n)}. In each we assume d>= 2 is a bound on the total
degree of the polynomials and l bounds the maximum binary encoding size of the
input.Comment: 28 pages, 10 figure
New Dependencies of Hierarchies in Polynomial Optimization
We compare four key hierarchies for solving Constrained Polynomial
Optimization Problems (CPOP): Sum of Squares (SOS), Sum of Diagonally Dominant
Polynomials (SDSOS), Sum of Nonnegative Circuits (SONC), and the Sherali Adams
(SA) hierarchies. We prove a collection of dependencies among these hierarchies
both for general CPOPs and for optimization problems on the Boolean hypercube.
Key results include for the general case that the SONC and SOS hierarchy are
polynomially incomparable, while SDSOS is contained in SONC. A direct
consequence is the non-existence of a Putinar-like Positivstellensatz for
SDSOS. On the Boolean hypercube, we show as a main result that Schm\"udgen-like
versions of the hierarchies SDSOS*, SONC*, and SA* are polynomially equivalent.
Moreover, we show that SA* is contained in any Schm\"udgen-like hierarchy that
provides a O(n) degree bound.Comment: 26 pages, 4 figure
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