11,163 research outputs found
Fast Second-order Cone Programming for Safe Mission Planning
This paper considers the problem of safe mission planning of dynamic systems
operating under uncertain environments. Much of the prior work on achieving
robust and safe control requires solving second-order cone programs (SOCP).
Unfortunately, existing general purpose SOCP methods are often infeasible for
real-time robotic tasks due to high memory and computational requirements
imposed by existing general optimization methods. The key contribution of this
paper is a fast and memory-efficient algorithm for SOCP that would enable
robust and safe mission planning on-board robots in real-time. Our algorithm
does not have any external dependency, can efficiently utilize warm start
provided in safe planning settings, and in fact leads to significant speed up
over standard optimization packages (like SDPT3) for even standard SOCP
problems. For example, for a standard quadrotor problem, our method leads to
speedup of 1000x over SDPT3 without any deterioration in the solution quality.
Our method is based on two insights: a) SOCPs can be interpreted as
optimizing a function over a polytope with infinite sides, b) a linear function
can be efficiently optimized over this polytope. We combine the above
observations with a novel utilization of Wolfe's algorithm to obtain an
efficient optimization method that can be easily implemented on small embedded
devices. In addition to the above mentioned algorithm, we also design a
two-level sensing method based on Gaussian Process for complex obstacles with
non-linear boundaries such as a cylinder
Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference
We propose a Branch-and-Cut (B&C) method for solving general MAP-MRF
inference problems. The core of our method is a very efficient bounding
procedure, which combines scalable semidefinite programming (SDP) and a
cutting-plane method for seeking violated constraints. In order to further
speed up the computation, several strategies have been exploited, including
model reduction, warm start and removal of inactive constraints.
We analyze the performance of the proposed method under different settings,
and demonstrate that our method either outperforms or performs on par with
state-of-the-art approaches. Especially when the connectivities are dense or
when the relative magnitudes of the unary costs are low, we achieve the best
reported results. Experiments show that the proposed algorithm achieves better
approximation than the state-of-the-art methods within a variety of time
budgets on challenging non-submodular MAP-MRF inference problems.Comment: 21 page
Info-Greedy sequential adaptive compressed sensing
We present an information-theoretic framework for sequential adaptive
compressed sensing, Info-Greedy Sensing, where measurements are chosen to
maximize the extracted information conditioned on the previous measurements. We
show that the widely used bisection approach is Info-Greedy for a family of
-sparse signals by connecting compressed sensing and blackbox complexity of
sequential query algorithms, and present Info-Greedy algorithms for Gaussian
and Gaussian Mixture Model (GMM) signals, as well as ways to design sparse
Info-Greedy measurements. Numerical examples demonstrate the good performance
of the proposed algorithms using simulated and real data: Info-Greedy Sensing
shows significant improvement over random projection for signals with sparse
and low-rank covariance matrices, and adaptivity brings robustness when there
is a mismatch between the assumed and the true distributions.Comment: Preliminary results presented at Allerton Conference 2014. To appear
in IEEE Journal Selected Topics on Signal Processin
Blending Learning and Inference in Structured Prediction
In this paper we derive an efficient algorithm to learn the parameters of
structured predictors in general graphical models. This algorithm blends the
learning and inference tasks, which results in a significant speedup over
traditional approaches, such as conditional random fields and structured
support vector machines. For this purpose we utilize the structures of the
predictors to describe a low dimensional structured prediction task which
encourages local consistencies within the different structures while learning
the parameters of the model. Convexity of the learning task provides the means
to enforce the consistencies between the different parts. The
inference-learning blending algorithm that we propose is guaranteed to converge
to the optimum of the low dimensional primal and dual programs. Unlike many of
the existing approaches, the inference-learning blending allows us to learn
efficiently high-order graphical models, over regions of any size, and very
large number of parameters. We demonstrate the effectiveness of our approach,
while presenting state-of-the-art results in stereo estimation, semantic
segmentation, shape reconstruction, and indoor scene understanding
A Doubly Nudged Elastic Band Method for Finding Transition States
A modification of the nudged elastic band (NEB) method is presented that
enables stable optimisations to be run using both the limited-memory
quasi-Newton (L-BFGS) and slow-response quenched velocity Verlet (SQVV)
minimisers. The performance of this new `doubly nudged' DNEB method is analysed
in conjunction with both minimisers and compared with previous NEB
formulations. We find that the fastest DNEB approach (DNEB/L-BFGS) can be
quicker by up to two orders of magnitude. Applications to permutational
rearrangements of the seven-atom Lennard-Jones cluster (LJ7) and highly
cooperative rearrangements of LJ38 and LJ75 are presented. We also outline an
updated algorithm for constructing complicated multi-step pathways using
successive DNEB runs.Comment: 13 pages, 8 figures, 2 table
Fast Exact Booleans for Iterated CSG using Octree-Embedded BSPs
We present octree-embedded BSPs, a volumetric mesh data structure suited for
performing a sequence of Boolean operations (iterated CSG) efficiently. At its
core, our data structure leverages a plane-based geometry representation and
integer arithmetics to guarantee unconditionally robust operations. These
typically present considerable performance challenges which we overcome by
using custom-tailored fixed-precision operations and an efficient algorithm for
cutting a convex mesh against a plane. Consequently, BSP Booleans and mesh
extraction are formulated in terms of mesh cutting. The octree is used as a
global acceleration structure to keep modifications local and bound the BSP
complexity. With our optimizations, we can perform up to 2.5 million mesh-plane
cuts per second on a single core, which creates roughly 40-50 million output
BSP nodes for CSG. We demonstrate our system in two iterated CSG settings:
sweep volumes and a milling simulation
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