We study two families of error-correcting codes defined in terms of very sparse matrices "MN" (MacKay-Neal (1995)) codes are recently invented, and "Gallager codes" were first investigated in 1962, but appear to have been largely forgotten, in spite of their excellent properties The decoding of both codes can be tackled with a practical sum-product algorithm We prove that these codes are "very good", in that sequences of codes exist which, when optimally decoded, achieve information rates up to the Shannon limit This result holds not only for the binary-symmetric channel but also for any channel with symmetric stationary ergodic noise We give experimental results for binary-symmetric channels and Gaussian channels demonstrating that practical performance substantially better than that of standard convolutional and concatenated codes can be achieved; indeed, the performance of Gallager codes is almost as close to the Shannon limit as that of turbo codes

/pdf/good-error-correcting-codes-based-on-very-sparse-matrices-3kdz0lhhm4.pdf

Good error-correcting codes based on very sparse matrices

The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.

/pdf/metaheuristics-in-combinatorial-optimization-overview-and-menr0q3bxw.pdf

Metaheuristics in combinatorial optimization: Overview and conceptual comparison

This comprehensive treatment of network information theory and its applications provides the first unified coverage of both classical and recent results. With an approach that balances the introduction of new models and new coding techniques, readers are guided through Shannon's point-to-point information theory, single-hop networks, multihop networks, and extensions to distributed computing, secrecy, wireless communication, and networking. Elementary mathematical tools and techniques are used throughout, requiring only basic knowledge of probability, whilst unified proofs of coding theorems are based on a few simple lemmas, making the text accessible to newcomers. Key topics covered include successive cancellation and superposition coding, MIMO wireless communication, network coding, and cooperative relaying. Also covered are feedback and interactive communication, capacity approximations and scaling laws, and asynchronous and random access channels. This book is ideal for use in the classroom, for self-study, and as a reference for researchers and engineers in industry and academia.

Network Information Theory

In this paper we review the most peculiar and interesting information-theoretic and communications features of fading channels. We first describe the statistical models of fading channels which are frequently used in the analysis and design of communication systems. Next, we focus on the information theory of fading channels, by emphasizing capacity as the most important performance measure. Both single-user and multiuser transmission are examined. Further, we describe how the structure of fading channels impacts code design, and finally overview equalization of fading multipath channels.

/pdf/fading-channels-information-theoretic-and-communications-2ipqbail97.pdf

Fading channels: information-theoretic and communications aspects

The pickup and delivery problem with time windows is the problem of serving a number of transportation requests using a limited amount of vehicles. Each request involves moving a number of goods from a pickup location to a delivery location. Our task is to construct routes that visit all locations such that corresponding pickups and deliveries are placed on the same route, and such that a pickup is performed before the corresponding delivery. The routes must also satisfy time window and capacity constraints.

This paper presents a heuristic for the problem based on an extension of the large neighborhood search heuristic previously suggested for solving the vehicle routing problem with time windows. The proposed heuristic is composed of a number of competing subheuristics that are used with a frequency corresponding to their historic performance. This general framework is denoted adaptive large neighborhood search.

The heuristic is tested on more than 350 benchmark instances with up to 500 requests. It is able to improve the best known solutions from the literature for more than 50% of the problems.

The computational experiments indicate that it is advantageous to use several competing subheuristics instead of just one. We believe that the proposed heuristic is very robust and is able to adapt to various instance characteristics.

/pdf/an-adaptive-large-neighborhood-search-heuristic-for-the-54a14dq2lk.pdf

An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows

Threshold accepting: a general purpose optimization algorithm appearing superior to simulated annealing

New optimization heuristics

http://polymorphe.free.fr/cours/ia/tsp/schrimpf-record_breaking_opt_ruin_recreate.pdf

Record Breaking Optimization Results Using the Ruin and Recreate Principle

The authors' main finding is that any object among doubly exponentially many objects can be identified in blocklength n with arbitrarily small error probability via a discrete memoryless channel (DMC), if randomization can be used for the encoding procedure. A novel doubly exponential coding theorem is presented which determines the optimal R, that is, the identification capacity of the DMC as a function of its transmission probability matrix. This identification capacity is a well-known quantity, namely, Shannon's transmission capacity for the DMC. >

/pdf/identification-via-channels-2rzsp1z8p1.pdf

Identification via channels

A study is made of the identification problem in the presence of a noiseless feedback channel, and the second-order capacity C/sub f/ (resp. C/sub F/) for deterministic (resp. randomized) encoding strategies is determined. Several important phenomena are encountered. (1) Although feedback does not increase the transmission capacity of a discrete memoryless channel (DMC), it does increase the (second-order) identification capacity; (2) noise increases C/sub f/; (3) the structure of the new capacity formulas is simpler than C.E. Shannon's (1948) familiar formula. This has the effect that proofs of converses become easier than in the authors' previous work. >

/pdf/identification-in-the-presence-of-feedback-a-discovery-of-t5psdjihxl.pdf

Gunter Dueck

Papers

Threshold accepting: a general purpose optimization algorithm appearing superior to simulated annealing

New optimization heuristics

Record Breaking Optimization Results Using the Ruin and Recreate Principle

Identification via channels

Identification in the presence of feedback-a discovery of new capacity formulas