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Piecewise linear homeomorphisms: The scalar case
 Proc. Int. Joint Conf. on Neural Networks
, 2000
"... endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution m ..."
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Cited by 6 (2 self)
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endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubspermissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. NOTE: At the time of publication, author Daniel Koditschek was affiliated with the University of Michigan. Currently, he is a faculty member in the
Invariant manifolds of hypercyclic vectors for the real scalar case
 Proc. Amer. Math. Soc
, 1999
"... Abstract. We show that every hypercyclic operator on a real locally convex vector space admits a dense invariant linear manifold of hypercyclic vectors. Given a locally convex vector space X and a continuous operator T: X − → X, we say that T is hypercyclic provided there exists some x ∈ X whose orb ..."
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Cited by 7 (1 self)
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complex locally convex spaces as well). We’d like to show here that the same holds for the real scalar case, by presenting a positive answer to the following question, raised by S. Ansari [1, Problem 1]: “Suppose X is a locally convex real vector space and T: X → X is a continuous linear operator with a
Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
 Evolutionary Computation
, 1994
"... In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about t ..."
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Cited by 520 (4 self)
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In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about
Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ¹ minimization
 PROC. NATL ACAD. SCI. USA 100 2197–202
, 2002
"... Given a ‘dictionary’ D = {dk} of vectors dk, we seek to represent a signal S as a linear combination S = ∑ k γ(k)dk, with scalar coefficients γ(k). In particular, we aim for the sparsest representation possible. In general, this requires a combinatorial optimization process. Previous work considered ..."
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Cited by 614 (38 self)
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Given a ‘dictionary’ D = {dk} of vectors dk, we seek to represent a signal S as a linear combination S = ∑ k γ(k)dk, with scalar coefficients γ(k). In particular, we aim for the sparsest representation possible. In general, this requires a combinatorial optimization process. Previous work
Chaotic quantized feedback stabilizers: the scalar case
 Comm. Inf. Sys
"... Dedicated to Sanjoy Mitter on the occasion of his 70th birthday. Abstract. In this paper we consider practical stabilization strategies of scalar linear systems by means of quantized feedback maps which use a minimal number of quantization levels. These stabilization schemes are based on the chaotic ..."
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Cited by 3 (2 self)
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Dedicated to Sanjoy Mitter on the occasion of his 70th birthday. Abstract. In this paper we consider practical stabilization strategies of scalar linear systems by means of quantized feedback maps which use a minimal number of quantization levels. These stabilization schemes are based
The General Rational Interpolation Problem in Scalar Case and Its Hankel Vector
"... This paper deals with the general rational interpolation problem (GRIP) in scalar case. In the recent work of Antoulas, Ball, Kang, and Willems the general solution to GRIP has been derived in the framework of linear fractions using the socalled generating matrix as the main tool. Within this frame ..."
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Cited by 1 (0 self)
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This paper deals with the general rational interpolation problem (GRIP) in scalar case. In the recent work of Antoulas, Ball, Kang, and Willems the general solution to GRIP has been derived in the framework of linear fractions using the socalled generating matrix as the main tool. Within
ANALYSIS OF MULTIPLE SCATTERING ITERATIONS FOR HIGHFREQUENCY SCATTERING PROBLEMS. II: THE THREEDIMENSIONAL SCALAR CASE
"... Analysis of multiple scattering iterations for highfrequency scattering problems. II: The threedimensional scalar case by ..."
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Analysis of multiple scattering iterations for highfrequency scattering problems. II: The threedimensional scalar case by
A symbolic approach to performance analysis of quantized feedback systems: The scalar case
 SIAM J. Control Optim
, 2005
"... When dealing with the control of a large number of interacting systems, the fact that the flow of information has to be limited becomes an essential feature of the control design. The first consequence of the limited information flow constraint is that the signals that the controllers and the system ..."
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analysis of the tradeoff between performance and information flow in the simple case of the stabilization of a scalar linear system by means of a memoryless quantized feedback map.
Stabilizing Quantized Feedback With Minimal Information Flow: The Scalar Case
 in Proc. 5th Int. Symp. Mathematical Theory of Networks and Systems, Univ. Notre Dame
, 2002
"... statefeedbac k with finitely many quantization levels yields only the soc#:WWD prac#D22: stabilization, namely the c#e vergenc# of any initial state belonging to a bigger bounded region into another smaller target region of the state spac#: The ratio between the measure of the starting region and th ..."
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statefeedbac k with finitely many quantization levels yields only the soc#:WWD prac#D22: stabilization, namely the c#e vergenc# of any initial state belonging to a bigger bounded region into another smaller target region of the state spac#: The ratio between the measure of the starting region and the target region isc#:DOW c#: trac#WL= of thec#e:xL loop system. In the analysis of the performanc# of a stabilization strategy based on a quantized statefeedbac k two parameters play ac#] tral role:the number of quantization levels used by thefeedbac k and the c#h vergenc# time of thec#e:D2 loop system. In this paper we propose a definition of optimality for a quantized stabilization strategy. This definition is based on how the number of quantization levels and the c#e vergenc# time grow with the c#e trac#j=]x Then, we analyze the performanc# and prove the optimality of three di#erent stabilizing quantizedfeedbac ks strategy forsc#:xD linear systems.
Learning from demonstration
 Advances in Neural Information Processing Systems 9
, 1997
"... By now it is widely accepted that learning a task from scratch, i.e., without any prior knowledge, is a daunting undertaking. Humans, however, rarely attempt to learn from scratch. They extract initial biases as well as strategies how to approach a learning problem from instructions and/or demonstra ..."
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Cited by 392 (32 self)
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speed up learning. In general nonlinear learning problems, only modelbased reinforcement learning shows significant speedup after a demonstration, while in the special case of linear quadratic regulator (LQR) problems, all methods profit from the demonstration. In an implementation of pole balancing
Results 1  10
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429,451