15,521 research outputs found
On weighted time optimal control for linear hybrid automata using quantifier elimination
This paper considers the optimal control problem for linear hybrid automata. In particular, it is shown that the problem can be transformed into a constrained optimization problem whose constraints are a set of inequalities with quantifiers. Quantifier Elimination (QE) techniques are employed in order to derive quantifier free inequalities that are linear. The optimal cost is obtained using linear programming. The optimal switching times and optimal continuous control inputs are computed and used in order to derive the optimal hybrid controller. Our results areapplied to an air traffic management example
On suboptimal control design for hybrid automata using predictive control techniques
In this paper we propose an on-line design technique for the target control problem, when the system is modelled by hybrid automata. First, we compute off-line the shortest path, which has the minimum discrete cost, from an initial state to the given target set. Next, we derive a controller which successfully drives the system from the initial state to the target set while minimizing a cost function. The model predictive control (MPC) technique is used when the current state is not within a guard set, otherwise the mixed-integer predictive control (MIPC) technique is employed. An on-line, semi-explicit control algorithm is derived by combining the two techniques. Finally, as an application of the proposed control procedure, the high-speed and energy-saving control problem of the CPU processing isconsidered
Robust semi-explicit model predictive control for hybrid automata
In this paper we propose an on-line design technique for the target control problem of hybrid automata. First, we compute on-line the shortest path, which has the minimum discrete cost, from an initial state to the given target set. Next, we derive a controller which successfully drives the system from the initial state to the target set while minimizing a cost function. The (robust) model predictive control (MPC) technique is used when the current state is not within a guard set, otherwise the (robust) mixed-integer predictive control (MIPC) technique is employed. An on-line, semi-explicit control algorithm is derived by combining the two techniques and applied on a high-speed and energy-saving control problem of the CPU processing
Declining Commodity Program Payments and Enhanced Environmental Regulations: Impacts on Acreage Allocation in the Great Plains
Environmental Economics and Policy,
A Hybrid Quantum Encoding Algorithm of Vector Quantization for Image Compression
Many classical encoding algorithms of Vector Quantization (VQ) of image
compression that can obtain global optimal solution have computational
complexity O(N). A pure quantum VQ encoding algorithm with probability of
success near 100% has been proposed, that performs operations 45sqrt(N) times
approximately. In this paper, a hybrid quantum VQ encoding algorithm between
classical method and quantum algorithm is presented. The number of its
operations is less than sqrt(N) for most images, and it is more efficient than
the pure quantum algorithm.
Key Words: Vector Quantization, Grover's Algorithm, Image Compression,
Quantum AlgorithmComment: Modify on June 21. 10pages, 3 figure
Real and Complex Monotone Communication Games
Noncooperative game-theoretic tools have been increasingly used to study many
important resource allocation problems in communications, networking, smart
grids, and portfolio optimization. In this paper, we consider a general class
of convex Nash Equilibrium Problems (NEPs), where each player aims to solve an
arbitrary smooth convex optimization problem. Differently from most of current
works, we do not assume any specific structure for the players' problems, and
we allow the optimization variables of the players to be matrices in the
complex domain. Our main contribution is the design of a novel class of
distributed (asynchronous) best-response- algorithms suitable for solving the
proposed NEPs, even in the presence of multiple solutions. The new methods,
whose convergence analysis is based on Variational Inequality (VI) techniques,
can select, among all the equilibria of a game, those that optimize a given
performance criterion, at the cost of limited signaling among the players. This
is a major departure from existing best-response algorithms, whose convergence
conditions imply the uniqueness of the NE. Some of our results hinge on the use
of VI problems directly in the complex domain; the study of these new kind of
VIs also represents a noteworthy innovative contribution. We then apply the
developed methods to solve some new generalizations of SISO and MIMO games in
cognitive radios and femtocell systems, showing a considerable performance
improvement over classical pure noncooperative schemes.Comment: to appear on IEEE Transactions in Information Theor
Mihai Gheorghiade, MD-Life and Concepts
How do you capture an idea, shape it, and then bring it into the world? Of his many talents, this ability was a fundamental characteristic of Mihai Gheorghiade. A quick glance through PubMed confirms his prodigious output, likely to overwhelm any novice or even expert scholar. His contribution to heart failure, especially acute heart failure (AHF), is profound, He authored several major concepts in acute heart failure, disseminated further by his students. Most concepts remained indelibly linked to his name: Digoxin trials research(1–3), AHFS (acute heart failure syndromes) definition(4), hemodynamic congestion(5), hospitalized heart failure (HHF) (6), the vulnerable phase(7,8), neutral hemodynamic agents(9), registries(10–12) and pre-trial registries(13), the “6-axis model”(14) and then the “8-axis model”(15). His work shaped the field of AHF
Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems
We propose a novel decomposition framework for the distributed optimization
of general nonconvex sum-utility functions arising naturally in the system
design of wireless multiuser interfering systems. Our main contributions are:
i) the development of the first class of (inexact) Jacobi best-response
algorithms with provable convergence, where all the users simultaneously and
iteratively solve a suitably convexified version of the original sum-utility
optimization problem; ii) the derivation of a general dynamic pricing mechanism
that provides a unified view of existing pricing schemes that are based,
instead, on heuristics; and iii) a framework that can be easily particularized
to well-known applications, giving rise to very efficient practical (Jacobi or
Gauss-Seidel) algorithms that outperform existing adhoc methods proposed for
very specific problems. Interestingly, our framework contains as special cases
well-known gradient algorithms for nonconvex sum-utility problems, and many
blockcoordinate descent schemes for convex functions.Comment: submitted to IEEE Transactions on Signal Processin
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