Solving linear programming problems exactly

A linear programming problem in an inequality form having a bounded solution is solved error-free using an algorithm that sorts the inequalities, removes the redundant ones, and uses the p-adic arithmetic. (C) Elsevier Science Inc., 1997

Translational and rotational motion in molecular liquids: A computer simulation study of Lennard-Jones ellipsoids

In order to understand the translational and rotational motion in dense molecular liquids, detailed molecular dynamics simulations of Lennard-Jones ellipsoids have been carried out for three different values of the aspect ratio kappa. For ellipsoids with an aspect ratio equal to 2, the product of the translational diffusion coefficient (D-T) and the average orientational correlation time of the l-th rank harmonics (tau(lR)), converges to a nearly constant value at high density. Surprisingly, this density independent value of D-T tau(lR) is within 5% of the hydrodynamic prediction with the slip boundary condition. This is despite the fact that both D-T and tau(lR) themselves change nearly by an order of magnitude in the density range considered, and the rotational correlation function itself is strongly nonexponential. For small aspect ratios (kappa less than or equal to 1.5), the rotational correlation function remains largely Gaussian even at a very large density, while for a large aspect ratio (kappa greater than or equal to 3), the transition to the nematic liquid-crystalline phase precludes the hydrodynamic regime. Thus, the rotational dynamics of ellipsoids show great sensitivity to the aspect ratio. At low density, tau(lR) goes through a minimum value, indicating the role of interactions in enhancing the rate of orientational relaxation. (C) 1997 American Institute of Physics. [S0021-9606(97)50142-5].

Decentralized Sustainable Energy Planning of Tumkur District, India

Present work shows the feasibility of decentralized energy options for the Tumkur district in India. Decentralized energy planning (DEP) involves scaling down energy planning to subnational or regional scales. The important aspect of the energy planning at decentralized level would be to prepare an area-based DEP to meet energy needs and development of alternate energy sources at least-cost to the economy and environment. The geographical coverage and scale reflects the level at which the analysis takes place, which is an important factor in determining the structure of models. In the present work, DEP modeling under different scenarios has been carried out for Tumkur district of India for the year 2020. DEP model is suitably scaled for obtaining the optimal mix of energy resources and technologies using a computer-based goal programming technique. The rural areas of the Tumkur district have different energy needs. Results show that electricity needs can be met by biomass gasifier technology, using biomass feedstock produced by allocating only 12% of the wasteland in the district at 8 t/ha/yr of biomass productivity. Surplus electricity can be produced by adopting the option of biomass power generation from energy plantations. The surplus electricity generated can be supplied to the grid. The sustainable development scenario is a least cost scenario apart from promoting self-reliance, local employment, and environmental benefits. (C) 2010 American Institute of Chemical Engineers Environ Prog, 30: 248-258, 2011

Optimal control of a stochastic hybrid system with discounted cost

We address the optimal control problem of a very general stochastic hybrid system with both autonomous and impulsive jumps. The planning horizon is infinite and we use the discounted-cost criterion for performance evaluation. Under certain assumptions, we show the existence of an optimal control. We then derive the quasivariational inequalities satisfied by the value function and establish well-posedness. Finally, we prove the usual verification theorem of dynamic programming.

Computer-Based Design Synthesis Research: An Overview

One of the hallmarks of engineering design is the design synthesis phase where the creativity of the designer most prominently comes into play as solutions are generated to meet underlying needs. Over the past decades, methodologies for generating concepts and design solutions have matured to the point that computation-based synthesis provides a means to explore a wider variety of solutions and take over more tedious design tasks. This paper reviews advances in function-based, grammar-based, and analogy-based synthesis approaches and their contributions to computational design synthesis research in the last decade. DOI: 10.1115/1.3593409]

Robust Formulations for Handling Uncertainty in Kernel Matrices

We study the problem of uncertainty in the entries of the Kernel matrix, arising in SVM formulation. Using Chance Constraint Programming and a novel large deviation inequality we derive a formulation which is robust to such noise. The resulting formulation applies when the noise is Gaussian, or has finite support. The formulation in general is non-convex, but in several cases of interest it reduces to a convex program. The problem of uncertainty in kernel matrix is motivated from the real world problem of classifying proteins when the structures are provided with some uncertainty. The formulation derived here naturally incorporates such uncertainty in a principled manner leading to significant improvements over the state of the art. 1.

Tatonnement Mechanisms for Combinatorial Exchanges

Combinatorial exchanges are double sided marketplaces with multiple sellers and multiple buyers trading with the help of combinatorial bids. The allocation and other associated problems in such exchanges are known to be among the hardest to solve among all economic mechanisms. It has been shown that the problems of surplus maximization or volume maximization in combinatorial exchanges are inapproximable even with free disposal. In this paper, the surplus maximization problem is formulated as an integer linear programming problem and we propose a Lagrangian relaxation based heuristic to find a near optimal solution. We develop computationally efficient tâtonnement mechanisms for clearing combinatorial exchanges where the Lagrangian multipliers can be interpreted as the prices of the items set by the exchange in each iteration. Our mechanisms satisfy Individual-rationality and Budget-nonnegativity properties. The computational experiments performed on representative data sets show that the proposed heuristic produces a feasible solution with negligible optimality gap.

Partial instantiation methods for inference in first-order logic

Satisfiability algorithms for propositional logic have improved enormously in recently years. This improvement increases the attractiveness of satisfiability methods for first-order logic that reduce the problem to a series of ground-level satisfiability problems. R. Jeroslow introduced a partial instantiation method of this kind that differs radically from the standard resolution-based methods. This paper lays the theoretical groundwork for an extension of his method that is general enough and efficient enough for general logic programming with indefinite clauses. In particular we improve Jeroslow's approach by (1) extending it to logic with functions, (2) accelerating it through the use of satisfiers, as introduced by Gallo and Rago, and (3) simplifying it to obtain further speedup. We provide a similar development for a "dual" partial instantiation approach defined by Hooker and suggest a primal-dual strategy. We prove correctness of the primal and dual algorithms for full first-order logic with functions, as well as termination on unsatisfiable formulas. We also report some preliminary computational results.

Scheduling expression trees for delayed-load architectures

In this paper we consider the problem of scheduling expression trees on delayed-load architectures. The problem tackled here takes root from the one considered in [Proceedings of the ACM SIGPLAN '91 Conf. on Programming Language Design and Implementation, 1991. p. 256] in which the leaves of the expression trees all refer to memory locations. A generalization of this involves the situation in which the trees may contain register variables, with the registers being used only at the leaves. Solutions to this generalization are given in [ACM Trans. Prog. Lang. Syst. 17 (1995) 740, Microproc. Microprog. 40 (1994) 577]. This paper considers the most general case in which the registers are reusable. This problem is tackled in [Comput. Lang, 21 (1995) 49] which gives an approximate solution to the problem under certain assumptions about the contiguity of the evaluation order: Here we propose an optimal solution (which may involve even a non-contiguous evaluation of the tree). The schedule generated by the algorithm given in this paper is optimal in the sense that it is an interlock-free schedule which uses the minimum number of registers required. An extension to the algorithm incorporates spilling. The problem as stated in this paper is an instruction scheduling problem. However, the problem could also be rephrased as an operations research problem with a difference in terminology. (C) 2002 Elsevier Science B.V. All rights reserved.

The restriction mapping problem revisited

In computational molecular biology, the aim of restriction mapping is to locate the restriction sites of a given enzyme on a DNA molecule. Double digest and partial digest are two well-studied techniques for restriction mapping. While double digest is NP-complete, there is no known polynomial-time algorithm for partial digest. Another disadvantage of the above techniques is that there can be multiple solutions for reconstruction. In this paper, we study a simple technique called labeled partial digest for restriction mapping. We give a fast polynomial time (O(n(2) log n) worst-case) algorithm for finding all the n sites of a DNA molecule using this technique. An important advantage of the algorithm is the unique reconstruction of the DNA molecule from the digest. The technique is also robust in handling errors in fragment lengths which arises in the laboratory. We give a robust O(n(4)) worst-case algorithm that can provably tolerate an absolute error of O(Delta/n) (where Delta is the minimum inter-site distance), while giving a unique reconstruction. We test our theoretical results by simulating the performance of the algorithm on a real DNA molecule. Motivated by the similarity to the labeled partial digest problem, we address a related problem of interest-the de novo peptide sequencing problem (ACM-SIAM Symposium on Discrete Algorithms (SODA), 2000, pp. 389-398), which arises in the reconstruction of the peptide sequence of a protein molecule. We give a simple and efficient algorithm for the problem without using dynamic programming. The algorithm runs in time O(k log k), where k is the number of ions and is an improvement over the algorithm in Chen et al. (C) 2002 Elsevier Science (USA). All rights reserved.

Qualitative theorem proving in linear constraints

We know, from the classical work of Tarski on real closed fields, that elimination is, in principle, a fundamental engine for mechanized deduction. But, in practice, the high complexity of elimination algorithms has limited their use in the realization of mechanical theorem proving. We advocate qualitative theorem proving, where elimination is attractive since most processes of reasoning take place through the elimination of middle terms, and because the computational complexity of the proof is not an issue. Indeed what we need is the existence of the proof and not its mechanization. In this paper, we treat the linear case and illustrate the power of this paradigm by giving extremely simple proofs of two central theorems in the complexity and geometry of linear programming.

Design and Implementation of a Flexible and Memory Efficient Operating System for Sensor Nodes

Sensor network nodes exhibit characteristics of both embedded systems and general-purpose systems.A sensor network operating system is a kind of embedded operating system, but unlike a typical embedded operating system, sensor network operatin g system may not be real time, and is constrained by memory and energy constraints. Most sensor network operating systems are based on event-driven approach. Event-driven approach is efficient in terms of time and space.Also this approach does not require a separate stack for each execution context. But using this model, it is difficult to implement long running tasks, like cryptographic operations. A thread based computation requires a separate stack for each execution context, and is less efficient in terms of time and space. In this paper, we propose a thread based execution model that uses only a fixed number of stacks. In this execution model, the number of stacks at each priority level are fixed. It minimizes the stack requirement for multi-threading environment and at the same time provides ease of programming. We give an implementation of this model in Contiki OS by separating thread implementation from protothread implementation completely. We have tested our OS by implementing a clock synchronization protocol using it.

A Nash Bargaining Approach to Retention Enhancing Bid Optimization in Sponsored Search Auctions with Discrete Bids

Bid optimization is now becoming quite popular in sponsored search auctions on the Web. Given a keyword and the maximum willingness to pay of each advertiser interested in the keyword, the bid optimizer generates a profile of bids for the advertisers with the objective of maximizing customer retention without compromising the revenue of the search engine. In this paper, we present a bid optimization algorithm that is based on a Nash bargaining model where the first player is the search engine and the second player is a virtual agent representing all the bidders. We make the realistic assumption that each bidder specifies a maximum willingness to pay values and a discrete, finite set of bid values. We show that the Nash bargaining solution for this problem always lies on a certain edge of the convex hull such that one end point of the edge is the vector of maximum willingness to pay of all the bidders. We show that the other endpoint of this edge can be computed as a solution of a linear programming problem. We also show how the solution can be transformed to a bid profile of the advertisers.

Faster Algorithms for Incremental Topological Ordering.Robert Tarjan

We present two online algorithms for maintaining a topological order of a directed acyclic graph as arcs are added, and detecting a cycle when one is created. Our first algorithm takes O(m 1/2) amortized time per arc and our second algorithm takes O(n 2.5/m) amortized time per arc, where n is the number of vertices and m is the total number of arcs. For sparse graphs, our O(m 1/2) bound improves the best previous bound by a factor of logn and is tight to within a constant factor for a natural class of algorithms that includes all the existing ones. Our main insight is that the two-way search method of previous algorithms does not require an ordered search, but can be more general, allowing us to avoid the use of heaps (priority queues). Instead, the deterministic version of our algorithm uses (approximate) median-finding; the randomized version of our algorithm uses uniform random sampling. For dense graphs, our O(n 2.5/m) bound improves the best previously published bound by a factor of n 1/4 and a recent bound obtained independently of our work by a factor of logn. Our main insight is that graph search is wasteful when the graph is dense and can be avoided by searching the topological order space instead. Our algorithms extend to the maintenance of strong components, in the same asymptotic time bounds.