Information-theoretical meaning of quantum dynamical entropy

The theory of noncommutative dynamical entropy and quantum symbolic dynamics for quantum dynamical systems is analised from the point of view of quantum information theory. Using a general quantum dynamical system as a communication channel one can define different classical capacities depending on the character of resources applied for encoding and decoding procedures and on the type of information sources. It is shown that for Bernoulli sources the entanglement-assisted classical capacity, which is the largest one, is bounded from above by the quantum dynamical entropy defined in terms of operational partitions of unity. Stronger results are proved for the particular class of quantum dynamical systems -- quantum Bernoulli shifts. Different classical capacities are exactly computed and the entanglement-assisted one is equal to the dynamical entropy in this case.Comment: 6 page

NCUWM Talk Abstracts 2010

Dr. Bryna Kra, Northwestern University “From Ramsey Theory to Dynamical Systems and Back” Dr. Karen Vogtmann, Cornell University “Ping-Pong in Outer Space” Lindsay Baun, College of St. Benedict Danica Belanus, University of North Dakota Hayley Belli, University of Oregon Tiffany Bradford, Saint Francis University Kathryn Bryant, Northern Arizona University Laura Buggy, College of St. Benedict Katharina Carella, Ithaca College Kathleen Carroll, Wheaton College Elizabeth Collins-Wildman, Carleton College Rebecca Dorff, Brigham Young University Melisa Emory, University of Nebraska at Omaha Avis Foster, George Mason University Xiaojing Fu, Clarkson University Jennifer Garbett, Kenyon College Nicki Gaswick, University of Nebraska-Lincoln Rita Gnizak, Fort Hays State University Kailee Gray, University of South Dakota Samantha Hilker, Sam Houston State University Ruthi Hortsch, University of Michigan Jennifer Iglesias, Harvey Mudd College Laura Janssen, University of Nebraska-Lincoln Laney Kuenzel, Stanford University Ellen Le, Pomona College Thu Le, University of the South Shauna Leonard, Arkansas State University Tova Lindberg, Bethany Lutheran College Lisa Moats, Concordia College Kaitlyn McConville, Westminster College Jillian Neeley, Ithaca College Marlene Ouayoro, George Mason University Kelsey Quarton, Bradley University Brooke Quisenberry, Hope College Hannah Ross, Kenyon College Karla Schommer, College of St. Benedict Rebecca Scofield, University of Iowa April Scudere, Westminster College Natalie Sheils, Seattle University Kaitlin Speer, Baylor University Meredith Stevenson, Murray State University Kiri Sunde, University of North Carolina Kaylee Sutton, John Carroll University Frances Tirado, University of Florida Anna Tracy, University of the South Kelsey Uherka, Morningside College Danielle Wheeler, Coe College Lindsay Willett, Grove City College Heather Williamson, Rice University Chengcheng Yang, Rice University Jie Zeng, Michigan Technological Universit

Applying multiobjective evolutionary algorithms in industrial projects

During the recent years, multiobjective evolutionary algorithms have matured as a flexible optimization tool which can be used in various areas of reallife applications. Practical experiences showed that typically the algorithms need an essential adaptation to the specific problem for a successful application. Considering these requirements, we discuss various issues of the design and application of multiobjective evolutionary algorithms to real-life optimization problems. In particular, questions on problem-specific data structures and evolutionary operators and the determination of method parameters are treated. As a major issue, the handling of infeasible intermediate solutions is pointed out. Three application examples in the areas of constrained global optimization (electronic circuit design), semi-infinite programming (design centering problems), and discrete optimization (project scheduling) are discussed

Structured pseudospectra and random eigenvalues problems in vibrating systems

This paper introduces the concept of pseudospectra as a generalized tool for uncertainty quantification and propagation in structural dynamics. Different types of pseudospectra of matrices and matrix polynomials are explained. Particular emphasis is given to structured pseudospectra for matrix polynomials, which offer a deterministic way of dealing with uncertainties for structural dynamic systems. The pseudospectra analysis is compared with the results from Monte Carlo simulations of uncertain discrete systems. Two illustrative example problems, one with probabilistic uncertainty with various types of statistical distributions and the other with interval type of uncertainty, are studied in detail. Excellent agreement is found between the pseudospectra results and Monte Carlo simulation results