5,313 research outputs found
Dynamic graph-based search in unknown environments
A novel graph-based approach to search in unknown environments is presented. A virtual geometric structure is imposed on the environment represented in computer memory by a graph. Algorithms use this representation to coordinate a team of robots (or entities). Local discovery of environmental features cause dynamic expansion of the graph resulting in global exploration of the unknown environment. The algorithm is shown to have O(k.nH) time
complexity, where nH is the number of vertices of the discovered environment and 1 <= k <= nH. A maximum bound on the length of the resulting walk is given
Global Optimization Using Local Search Approach for Course Scheduling Problem
Course scheduling problem is a combinatorial optimization problem which is defined over a finite discrete problem whose candidate solution structure is expressed as a finite sequence of course events scheduled in available time and space resources. This problem is considered as non-deterministic polynomial complete problem which is hard to solve. Many solution methods have been studied in the past for solving the course scheduling problem, namely from the most traditional approach such as graph coloring technique; the local search family such as hill-climbing search, taboo search, and simulated annealing technique; and various population-based metaheuristic methods such as evolutionary algorithm, genetic algorithm, and swarm optimization. This article will discuss these various probabilistic optimization methods in order to gain the global optimal solution. Furthermore, inclusion of a local search in the population-based algorithm to improve the global solution will be explained rigorously
Balancing Global Exploration and Local-connectivity Exploitation with Rapidly-exploring Random disjointed-Trees
Sampling efficiency in a highly constrained environment has long been a major
challenge for sampling-based planners. In this work, we propose
Rapidly-exploring Random disjointed-Trees* (RRdT*), an incremental optimal
multi-query planner. RRdT* uses multiple disjointed-trees to exploit
local-connectivity of spaces via Markov Chain random sampling, which utilises
neighbourhood information derived from previous successful and failed samples.
To balance local exploitation, RRdT* actively explore unseen global spaces when
local-connectivity exploitation is unsuccessful. The active trade-off between
local exploitation and global exploration is formulated as a multi-armed bandit
problem. We argue that the active balancing of global exploration and local
exploitation is the key to improving sample efficient in sampling-based motion
planners. We provide rigorous proofs of completeness and optimal convergence
for this novel approach. Furthermore, we demonstrate experimentally the
effectiveness of RRdT*'s locally exploring trees in granting improved
visibility for planning. Consequently, RRdT* outperforms existing
state-of-the-art incremental planners, especially in highly constrained
environments.Comment: Submitted to IEEE International Conference on Robotics and Automation
(ICRA) 201
Adaptive Momentum for Neural Network Optimization
In this thesis, we develop a novel and efficient algorithm for optimizing neural networks inspired by a recently proposed geodesic optimization algorithm. Our algorithm, which we call Stochastic Geodesic Optimization (SGeO), utilizes an adaptive coefficient on top of Polyaks Heavy Ball method effectively controlling the amount of weight put on the previous update to the parameters based on the change of direction in the optimization path. Experimental results on strongly convex functions with Lipschitz gradients and deep Autoencoder benchmarks show that SGeO reaches lower errors than established first-order methods and competes well with lower or similar errors to a recent second-order method called K-FAC (Kronecker-Factored Approximate Curvature). We also incorporate Nesterov style lookahead gradient into our algorithm (SGeO-N) and observe notable improvements. We believe that our research will open up new directions for high-dimensional neural network optimization where combining the efficiency of first-order methods and the effectiveness of second-order methods proves a promising avenue to explore
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