This is the eleventh post in an article series about MITs lecture course Introduction to Algorithms. In this post I will review lecture sixteen, which introduces the concept of Greedy Algorithms, reviews Graphs and applies the greedy Prims Algorithm to the Minimum Spanning Tree (MST) Problem.. The previous lecture introduced dynamic programming. Dynamic programming was used for finding solutions to optimization problems. In such problems there can be many possible solutions. Each solution has a value, and we want to find a solution with the optimal (minimum or maximum) value. Greedy algorithms are another set of methods for finding optimal solution. A greedy algorithm always makes the choice that looks best at the moment. That is, it makes a locally optimal choice in the hope that this choice will lead to a globally optimal solution. Greedy algorithms do not always yield optimal solutions, but for many problems they do. In this lecture it is shown that a greedy algorithm gives an optimal ...
Feature selection is a useful tool for identifying which features, or attributes, of a dataset cause or explain the phenomena that the dataset describes, and improving the efficiency and accuracy of learning algorithms for discovering such phenomena. Consequently, feature selection has been studied intensively in machine learning research. However, while feature selection algorithms that exhibit excellent accuracy have been developed, they are seldom used for analysis of high-dimensional data because high-dimensional data usually include too many instances and features, which make traditional feature selection algorithms inefficient. To eliminate this limitation, we tried to improve the run-time performance of two of the most accurate feature selection algorithms known in the literature. The result is two accurate and fast algorithms, namely sCwc and sLcc. Multiple experiments with real social media datasets have demonstrated that our algorithms improve the performance of their original algorithms
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Analysis of genomes evolving by inversions leads to a general combinatorial problem of Sorting by Reversals , MIN-SBR, the problem of sorting a permutation by a minimum number of reversals. Following a series of preliminary results, Hannenhalli and Pevzner developed the first exact polynomial time algorithm for the problem of sorting signed permutations by reversals, and a polynomial time algorithm for a special case of unsigned permutations. The best known approximation algorithm for MIN-SBR, due to Christie, gives a performance ratio of 1.5. In this paper, by exploiting the polynomial time algorithm for sorting signed permutations and by developing a new approximation algorithm for maximum cycle decomposition of breakpoint graphs, we design a new 1.375-algorithm for the MIN-SBR problem.. ...
Reverse engineering of gene regulatory networks using information theory models has received much attention due to its simplicity, low computational cost, and capability of inferring large networks. One of the major problems with information theory models is to determine the threshold that defines the regulatory relationships between genes. The minimum description length (MDL) principle has been implemented to overcome this problem. The description length of the MDL principle is the sum of model length and data encoding length. A user-specified fine tuning parameter is used as control mechanism between model and data encoding, but it is difficult to find the optimal parameter. In this work, we propose a new inference algorithm that incorporates mutual information (MI), conditional mutual information (CMI), and predictive minimum description length (PMDL) principle to infer gene regulatory networks from DNA microarray data. In this algorithm, the information theoretic quantities MI and CMI determine the
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolution-based methods have been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate subdiscipline combining the fields of evolutionary computation and classical multiple criteria decision making. In this paper, the basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective. The focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms. Different techniques to implement these strongly related concepts will be discussed, and further important aspects such as constraint handling and
Efficient Risk Profiling Using Bayesian Networks and Particle Swarm Optimization Algorithm: 10.4018/978-1-4666-9458-3.ch004: Chapter introduce usage of particle swarm optimization algorithm and explained methodology, as a tool for discovering customer profiles based on previously
Particle Swarm Optimization Algorithm as a Tool for Profiling from Predictive Data Mining Models: 10.4018/978-1-5225-0788-8.ch033: This chapter introduces the methodology of particle swarm optimization algorithm usage as a tool for finding customer profiles based on a previously developed
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This volume emphasises on theoretical results and algorithms of combinatorial optimization with provably good performance, in contrast to heuristics. It documents the relevant knowledge on combinatorial optimization and records those problems and algorithms of this discipline.Korte, Bernhard is the author of Combinatorial Optimization Theory and Algorithms, published 2005 under ISBN 9783540256847 and ISBN 3540256849. [read more] ...
DNA computing is a new computing paradigm which uses bio-molecular as information storage media and biochemical tools as information processing operators. It has shows many successful and promising results for various applications. Since DNA reactions are probabilistic reactions, it can cause the different results for the same situations, which can be regarded as errors in the computation. To overcome the drawbacks, much works have focused to design the error-minimized DNA sequences to improve the reliability of DNA computing. In this research, Population-based Ant Colony Optimization (P-ACO) is proposed to solve the DNA sequence optimization. PACO approach is a meta-heuristic algorithm that uses some ants to obtain the solutions based on the pheromone in their colony. The DNA sequence design problem is modelled by four nodes, representing four DNA bases (A, T, C, and G). The results from the proposed algorithm are compared with other sequence design methods, which are Genetic Algorithm (GA), ...
In machine learning, weighted majority algorithm (WMA) is a meta-learning algorithm used to construct a compound algorithm from a pool of prediction algorithms, which could be any type of learning algorithms, classifiers, or even real human experts.[1][2] The algorithm assumes that we have no prior knowledge about the accuracy of the algorithms in the pool, but there are sufficient reasons to believe that one or more will perform well. Assume that the problem is a binary decision problem. To construct the compound algorithm, a positive weight is given to each of the algorithms in the pool. The compound algorithm then collects weighted votes from all the algorithms in the pool, and gives the prediction that has a higher vote. If the compound algorithm makes a mistake, the algorithms in the pool that contributed to the wrong predicting will be discounted by a certain ratio β where 0,β,1. It can be shown that the upper bounds on the number of mistakes made in a given sequence of predictions from ...
In mathematics, the greedy algorithm for Egyptian fractions is a greedy algorithm, first described by Fibonacci, for transforming rational numbers into Egyptian fractions. An Egyptian fraction is a representation of an irreducible fraction as a sum of distinct unit fractions, as e.g. 5/6 = 1/2 + 1/3. As the name indicates, these representations have been used as long ago as ancient Egypt, but the first published systematic method for constructing such expansions is described in the Liber Abaci (1202) of Leonardo of Pisa (Fibonacci). It is called a greedy algorithm because at each step the algorithm chooses greedily the largest possible unit fraction that can be used in any representation of the remaining fraction. Fibonacci actually lists several different methods for constructing Egyptian fraction representations (Sigler 2002, chapter II.7). He includes the greedy method as a last resort for situations when several simpler methods fail; see Egyptian fraction for a more detailed listing of these ...
TY - JOUR. T1 - Multiobjective process planning and scheduling using improved vector evaluated genetic algorithm with archive. AU - Zhang, Wenqiang. AU - Fujimura, Shigeru. PY - 2012/5. Y1 - 2012/5. N2 - Multiobjective process planning and scheduling (PPS) is a most important practical but very intractable combinatorial optimization problem in manufacturing systems. Many researchers have used multiobjective evolutionary algorithms (moEAs) to solve such problems; however, these approaches could not achieve satisfactory results in both efficacy (quality, i.e., convergence and distribution) and efficiency (speed). As classical moEAs, nondominated sorting genetic algorithm II (NSGA-II) and SPEA2 can get good efficacy but need much CPU time. Vector evaluated genetic algorithm (VEGA) also cannot be applied owing to its poor efficacy. This paper proposes an improved VEGA with archive (iVEGA-A) to deal with multiobjective PPS problems, with consideration being given to the minimization of both makespan ...
In this thesis we focus on subexponential algorithms for NP-hard graph problems: exact and parameterized algorithms that have a truly subexponential running time behavior. For input instances of size n we study exact algorithms with running time 2O(√n) and parameterized algorithms with running time 2O(√k) ·nO(1) with parameter k, respectively. We study a class of problems for which we design such algorithms for three different types of graph classes: planar graphs, graphs of bounded genus, and H-minor-free graphs. We distinguish between unconnected and connected problems, and discuss how to conceive parameterized and exact algorithms for such problems. We improve upon existing dynamic programming techniques used in algorithms solving those problems. We compare tree-decomposition and branch-decomposition based dynamic programming algorithms and show how to unify both algorithms to one single algorithm. Then we give a dynamic programming technique that reduces much of the computation involved ...
The discovery of single-nucleotide polymorphisms (SNPs) has important implications in a variety of genetic studies on human diseases and biological functions. One valuable approach proposed for SNP discovery is based on base-specific cleavage and mass spectrometry. However, it is still very challenging to achieve the full potential of this SNP discovery approach. In this study, we formulate two new combinatorial optimization problems. While both problems are aimed at reconstructing the sample sequence that would attain the minimum number of SNPs, they search over different candidate sequence spaces. The first problem, denoted as , limits its search to sequences whose in silico predicted mass spectra have all their signals contained in the measured mass spectra. In contrast, the second problem, denoted as
The phase retrieval problem is of paramount importance in various areas of applied physics and engineering. The state of the art for solving this problem in two dimensions relies heavily on the pioneering work of Gerchberg, Saxton, and Fienup. Despite the widespread use of the algorithms proposed by these three researchers, current mathematical theory cannot explain their remarkable success. Nevertheless, great insight can be gained into the behavior, the shortcomings, and the performance of these algorithms from their possible counterparts in convex optimization theory. An important step in this direction was made two decades ago when the error reduction algorithm was identified as a nonconvex alternating projection algorithm. Our purpose is to formulate the phase retrieval problem with mathematical care and to establish new connections between well-established numerical phase retrieval schemes and classical convex optimization methods. Specifically, it is shown that Fienups basic input-output ...
Unsupervised image segmentation is an important component in many image understanding algorithms and practical vision systems. However, evaluation of segmentation algorithms thus far has been largely subjective, leaving a system designer to judge the effectiveness of a technique based only on intuition and results in the form of a few example segmented images. This is largely due to image segmentation being an ill-defined problem-there is no unique ground-truth segmentation of an image against which the output of an algorithm may be compared. This paper demonstrates how a recently proposed measure of similarity, the normalized probabilistic rand (NPR) index, can be used to perform a quantitative comparison between image segmentation algorithms using a hand-labeled set of ground-truth segmentations. We show that the measure allows principled comparisons between segmentations created by different algorithms, as well as segmentations on different images. We outline a procedure for algorithm ...
Course Description: In this course students will learn about parallel algorithms. The emphasis will be on algorithms that can be used on shared-memory parallel machines such as multicore architectures. The course will include both a theoretical component and a programming component. Topics to be covered include: modeling the cost of parallel algorithms, lower-bounds, and parallel algorithms for sorting, graphs, computational geometry, and string operations. The programming language component will include data-parallelism, threads, futures, scheduling, synchronization types, transactional memory, and message passing. Course Requirements: There will be bi-weekly assignments, two exams (midterm and final), and a final project. Each student will be required to scribe one lecture. Your grade will be partitioned into: 10% scribe notes, 40% assignments, 20% project, 15% midterm, 15% final. Policies: For homeworks, unless stated otherwise, you can look up material on the web and books, but you cannot ...
The article presents a general view of a class of decomposition algorithms for training Support Vector Machines (SVM) which are motivated by the method of feasible directions. The first such algorithm for the pattern recognition SVM has been proposed by Joachims in 1999. Its extension to the regression SVM – the maximal inconsistency algorithm – has been recently presented by the author. A detailed account of both algorithms is carried out, complemented by theoretical investigation of the relationship between the two algorithms. It is proved that the two algorithms are equivalent for the pattern recognition SVM, and the feasible direction interpretation of the maximal inconsistency algorithm is given for the regression SVM. The experimental results demonstrate an order of magnitude decrease of training time in comparison with training without decomposition, and, most importantly, provide experimental evidence of the linear
Downloadable (with restrictions)! This paper introduces a second-order differentiability smoothing technique to the classical l 1 exact penalty function for constrained optimization problems(COP). Error estimations among the optimal objective values of the nonsmooth penalty problem, the smoothed penalty problem and the original optimization problem are obtained. Based on the smoothed problem, an algorithm for solving COP is proposed and some preliminary numerical results indicate that the algorithm is quite promising. Copyright Springer Science+Business Media, LLC 2013
The Parallel Algorithms Project conducts a dedicated research to address the solution of problems in applied mathematics by proposing advanced numerical algorithms to be used on massively parallel computing platforms. The Parallel Algorithms Project is especially considering problems known to be out of reach of standard current numerical methods due to, e.g., the large-scale nature or the nonlinearity of the problem, the stochastic nature of the data, or the practical constraint to obtain reliable numerical results in a limited amount of computing time. This research is mostly performed in collaboration with other teams at CERFACS and the shareholders of CERFACS as outlined in this report.. This research roadmap is known to be quite ambitious and we note that the major research topics have evolved over the past years. The main current focus concerns both the design of algorithms for the solution of sparse linear systems coming from the discretization of partial differential equations and the ...
A global optimization approach for the factor analysis of wireline logging data sets is presented. Oilfield well logs are processed together to give an estimate to factor logs by using an adaptive genetic algorithm. Nonlinear relations between the first factor and essential petrophysical parameters of shaly-sand reservoirs are revealed, which are used to predict the values of shale volume and permeability directly from the factor scores. Independent values of the relevant petrophysical properties are given by inverse modeling and well-known deterministic methods. Case studies including the evaluation of hydrocarbon formations demonstrate the feasibility of the improved algorithm of factor analysis. Comparative numerical analysis made between the genetic algorithm-based factor analysis procedure and the independent well log analsis methods shows consistent results. By factor analysis, an independent in-situ estimate to shale content and permeability is given, which may improve the reservoir model and
This paper describes optimal location and sizing of static var compensator (SVC) based on Particle Swarm Optimization for minimization of transmission losses considering cost function. Particle Swarm Optimization (PSO) is population-based stochastic search algorithms approaches as the potential techniques to solving such a problem. For this study, static var compensator (SVC) is chosen as the compensation device. Validation through the implementation on the IEEE 30-bus system indicated that PSO is feasible to achieve the task. The simulation results are compared with those obtained from Evolutionary Programming (EP) technique in the attempt to highlight its merit.. ...
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): In this paper, we present a new strongly polynomial time algorithm for the minimum cost flow problem, based on a refinement of the Edmonds-Karp scaling technique. Our algorithm solves the uncapacitated minimum cost flow problem as a sequence of O(n log n) shortest path problems on networks with n nodes and m arcs and runs in O(n log n (m + n log n)) time. Using a standard transformation, thjis approach yields an O(m log n (m + n log n)) algorithm for the capacitated minimum cost flow problem. This algorithm improves the best previous strongly polynomial time algorithm, due to Z. Galil and E. Tardos, by a factor of n 2 /m. Our algorithm for the capacitated minimum cost flow problem is even more efficient if the number of arcs with finite upper bounds, say n, is much less than m. In this case, the running time of the algorithm is O((m + n)log n(m + n log n)).
A multiscale design and multiobjective optimization procedure is developed to design a new type of graded cellular hip implant. We assume that the prosthesis design domain is occupied by a unit cell representing the building block of the implant. An optimization strategy seeks the best geometric parameters of the unit cell to minimize bone resorption and interface failure, two conflicting objective functions. Using the asymptotic homogenization method, the microstructure of the implant is replaced by a homogeneous medium with an effective constitutive tensor. This tensor is used to construct the stiffness matrix for the finite element modeling (FEM) solver that calculates the value of each objective function at each iteration. As an example, a 2D finite element model of a left implanted femur is developed. The relative density of the lattice material is the variable of the multiobjective optimization, which is solved through the non-dominated sorting genetic algorithm II (NSGA-II). The set of ...
However, there is no reason that you should be limited to one algorithm in your solutions. Experienced analysts will sometimes use one algorithm to determine the most effective inputs (that is, variables), and then apply a different algorithm to predict a specific outcome based on that data. SQL Server data mining lets you build multiple models on a single mining structure, so within a single data mining solution you might use a clustering algorithm, a decision trees model, and a naïve Bayes model to get different views on your data. You might also use multiple algorithms within a single solution to perform separate tasks: for example, you could use regression to obtain financial forecasts, and use a neural network algorithm to perform an analysis of factors that influence sales.. ...
We propose a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for optimization. Our method iteratively transports a set of particles to match the target distribution, by applying a form of functional gradient descent that minimizes the KL divergence. Empirical studies are performed on various real world models and datasets, on which our method is competitive with existing state-of-the-art methods. The derivation of our method is based on a new theoretical result that connects the derivative of KL divergence under smooth transforms with Steins identity and a recently proposed kernelized Stein discrepancy, which is of independent interest. |P /|
Follicular patterned lesions of the thyroid are problematic and interpretation is often subjective. While thyroid experts are comfortable with their own criteria and thresholds, those encountering these lesions sporadically have a degree of uncertainty with a proportion of cases. The purpose of this review is to highlight the importance of proper diligent sampling of an encapsulated thyroid lesion (in totality in many cases), examination for capsular and vascular invasion, and finally the assessment of nuclear changes that are pathognomonic of papillary thyroid carcinoma (PTC). Based on these established criteria, an algorithmic approach is suggested using known, accepted terminology. The importance of unequivocal, clear-cut nuclear features of PTC as opposed to inconclusive features is stressed. If the nuclear features in an encapsulated, non-invasive follicular patterned lesion fall short of those encountered in classical PTC, but nonetheless are still worrying or concerning, the term ...
View published article Trauma to Lisfrancs Joint, An Algorithmic Approach, published in Lower Extremity Review by Amol Saxena DPM, Palo Alto, CA. Dr Saxena specializes in sports medicine and surgery of the foot and ankle.
We study the problem of finding the cycle of minimum cost-to-time ratio in a directed graph with n nodes and m edges. This problem has a long history in combinatorial optimization and has recently seen interesting applications in the context of quantitative verification. We focus on strongly polynomial algorithms to cover the use-case where the weights are relatively large compared to the size of the graph. Our main result is an algorithm with running time ~O(m^{3/4} n^{3/2}), which gives the first improvement over Megiddos ~O(n^3) algorithm [JACM83] for sparse graphs (We use the notation ~O(.) to hide factors that are polylogarithmic in n.) We further demonstrate how to obtain both an algorithm with running time n^3/2^{Omega(sqrt(log n)} on general graphs and an algorithm with running time ~O(n) on constant treewidth graphs. To obtain our main result, we develop a parallel algorithm for negative cycle detection and single-source shortest paths that might be of independent interest ...
Algorithm portfolios are known to offer robust performances, efficiently overcoming the weakness of every single algorithm on some particular problem instances. Two complementary approaches to get the best out of an algorithm portfolio is to achieve algorithm selection (AS), and to define a scheduler, sequentially launching a few algorithms on a limited computational budget each. The presented Algorithm Selector And Prescheduler system relies on the joint optimization of a pre-scheduler and a per instance AS, selecting an algorithm well-suited to the problem instance at hand. ASAP has been thoroughly evaluated against the state-of-the-art during the ICON challenge for algorithm selection, receiving an honourable mention. Its evaluation on several combinatorial optimization benchmarks exposes surprisingly good results of the simple heuristics used; some extensions thereof are presented and discussed in the paper.
Basic concepts. Definition and specification of algorithms. Computational complexity and asymptotic estimates of running time. Sorting algorithms and divide and conquer algorithms. Graphs and networks. Basic graph theory definitions. Algorithms for the reachability problem in a graph. Spanning trees. Algorithms for finding a minimum-cost spanning tree in a graph. Shortest paths. Algorithms for finding one or more shortest paths in graph with nonnegative arc or general arc lengths but not negative length circuits. Network flow algorithms. Flows in capacitated networks, algorithms to find the maximum flow in a network and max-flow min-cut theorems. Matchings. Weighted and unweighted matchings in bipartite graphs, algorithms to find a maximum weight/cardinality matching, the Koenig-Egervary theorem and its relationship with the vertex cover problem. Computational complexity theory. The P and NP classes. Polynomial reductions. NP-completeness and NP-hardness. Exponential-time algorithms. Implicit ...
Current face recognition algorithms use hand-crafted features or extract features by deep learning. This paper presents a face recognition algorithm based on improved deep networks that can automatically extract the discriminative features of the target more accurately. Firstly,this algorithm uses ZCA( Zero-mean Component Analysis) whitening to preprocess the input images in order to reduce the correlation between features and the complexity of the training networks.Then,it organically combines convolution,pooling and stacked sparse autoencoder to get a deep network feature extractor.The convolution kernels are achieved through a separate unsupervised learning model. The improved deep networks get an automatic deep feature extractor through preliminary training and fine-tuning. Finally,the softmax regression model is used to classify the extracted features. This algorithm is tested on several commonly used face databases. It is indicated that the performance is better than the traditional methods and
In the paper we present some guidelines for the application of nonparametric statistical tests and post-hoc procedures devised to perform multiple comparisons of machine learning algorithms. We emphasize that it is necessary to distinguish between pairwise and multiple comparison tests. We show that the pairwise Wilcoxon test, when employed to multiple comparisons, will lead to overoptimistic conclusions. We carry out intensive normality examination employing ten different tests showing that the output of machine learning algorithms for regression problems does not satisfy normality requirements. We conduct experiments on nonparametric statistical tests and post-hoc procedures designed for multiple 1 × N and N × N comparisons with six different neural regression algorithms over 29 benchmark regression data sets. Our investigation proves the usefulness and strength of multiple comparison statistical procedures to analyse and select machine learning algorithms ...
This paper describes a parallel genetic algorithm developed for the solution of the set partitioning problem- a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steady-state genetic algorithm on their own subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty real-world set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. We found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, high- quality integer feasible solutions were found for problems with 36,
This paper focuses on the iterative parameter estimation algorithms for dual-frequency signal models that are disturbed by stochastic noise. The key of the work is to overcome the difficulty that the signal model is a highly nonlinear function with respect to frequencies. A gradient-based iterative (GI) algorithm is presented based on the gradient search. In order to improve the estimation accuracy of the GI algorithm, a Newton iterative algorithm and a moving data window gradient-based iterative algorithm are proposed based on the moving data window technique. Comparative simulation results are provided to illustrate the effectiveness of the proposed approaches for estimating the parameters of signal models.
Improving the Performance of the RISE Algorithm - Ideally, a multi-strategy learning algorithm performs better than its component approaches. RISE is a multi-strategy algorithm that combines rule induction and instance-based learning. It achieves higher accuracy than some state-of-the-art learning algorithms, but for large data sets it has a very high average running time. This work presents the analysis and experimental evaluation of SUNRISE, a new multi-strategy learning algorithm based on RISE. The SUNRISE algorithm was developed to be faster than RISE with similar accuracy. Comparing the results of the experimental evaluation of the two algorithms, it could be verified that the new algorithm achieves comparable accuracy to that of the RISE algorithm but in a lower average running time.
This paper proposes two parallel algorithms called an even region parallel algorithm (ERPA) and an even strip parallel algorithm (ESPA) respectively for ex
NIPS 2013 Workshop on Greedy Algorithms, Frank-Wolfe and Friends - A modern perspective Keywords: Frank-Wolfe Algorithm, greedy algorithms, first-order optimization, convex optimization, signal processing, machine learning
In this paper we present a robust parsing algorithm based on the link grammar formalism for parsing natural languages. Our algorithm is a natural extension of the original dynamic programming recognition algorithm which recursively counts the number of linkages between two words in the input sentence. The modified algorithm uses the notion of a null link in order to allow a connection between any pair of adjacent words, regardless of their dictionary definitions. The algorithm proceeds by making three dynamic programming passes. In the first pass, the input is parsed using the original algorithm which enforces the constraints on links to ensure grammaticality. In the second pass, the total cost of each substring of words is computed, where cost is determined by the number of null links necessary to parse the substring. The final pass counts the total number of parses with minimal cost. All of the original pruning techniques have natural counterparts in the robust algorithm. When used together ...
This paper presents an implementation of three Genetic Algorithm models for solving a reliability optimization problem for a redundancy system with several failure modes, a modification on a parallel a genetic algorithm model and a new parallel genetic algorithm model. These three models are: a sequential model, a modified global parallel genetic algorithm model and a new proposed parallel genetic algorithm model we called the Trigger Model (TM). The reduction of the implementation processing time is the basic motivation of genetic algorithms parallelization. In this work, parallel virtual machine (PVM), which is a portable message-passing programming system, designed to link separate host machines to form a virtual machine which is a single, manageable computing resource, is used in a distributed heterogeneous environment. The best result was reached and The TM model was clearly performing better than the other two models. ...
Some simple algorithms commonly used in computer science are linear search algorithms, arrays and bubble sort algorithms. Insertion sorting algorithms are also often used by computer...
The LDDMM Validation section provides input data, processing and visualization examples for LDDMM to ensure correctness of the resultant data. These examples are useful tests when LDDMM is run on new environments or platforms. Example images show atlas volume in red. On the left, the original target is in grey. On the right, the deformed atlas is pictured. A sample LDDMM command is posted with each example (click here or type ...
A system providing for user intervention in a medical control arrangement may comprise a first user intervention mechanism responsive to user selection thereof to produce a first user intervention signal, a second user intervention mechanism responsive to user selection thereof to produce a second user intervention signal, and a processor executing a drug delivery algorithm forming part of the medical control arrangement. The processor may be responsive to the first user intervention signal to include an intervention therapy value in the execution of the drug delivery algorithm, and responsive to the second user intervention signal to exclude the intervention therapy value from the execution of the drug delivery algorithm. The medical control arrangement may be a diabetes control arrangement, the drug delivery algorithm may be an insulin delivery algorithm, and the intervention therapy value may be, for example, an intervention insulin quantity or an intervention carbohydrate quantity.
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Total variation (TV) regularization is popular in image reconstruction due to its edge-preserving property. In this paper, we extend the alternating minimization algorithm recently proposed in [37] to the case of recovering images from random projections. Specifically, we propose to solve the TV regularized least squares problem by alternating minimization algorithms based on the classical quadratic penalty technique and alternating minimization of the augmented Lagrangian function. The per-iteration cost of the proposed algorithms is dominated by two matrix-vector multiplications and two fast Fourier transforms. Convergence results, including finite convergence of certain variables and $q$-linear convergence rate, are established for the quadratic penalty method. Furthermore, we compare numerically the new algorithms with some state-of-the-art algorithms. Our experimental results indicate that the new algorithms are stable, efficient and competitive with the compared ones.
inproceedings{GaMi87, Author="Hillel Gazit and Gary L. Miller", title="A Parallel Algorithm for Finding a Separator in Planar Graphs", booktitle=FOCS28, year="1987", pages="238--248", organization="IEEE", address="Los Angeles", month="October", misc="Submitted 6-1-87.", bib2html_rescat = {Parallel Algorithms,Graph Algorithms,Graph Separators,Planar Graph Algorithms}, thanks="NSF DCR-8514961 ...
By Chang, Hsu-Hwa Chen, Yan-Kwang; Chen, Mu-Chen Parameter design is the most important phase in the development of new products and processes, especially in regards to dynamic systems. Statistics-based approaches are usually employed to address dynamic parameter design problems; however, these approaches have some limitations when applied to dynamic systems with continuous control factors. This study proposes a novel three-phase approach for resolving the dynamic parameter design problems as well as the static characteristic problems, which combines continuous ant colony optimisation (CACO) with neural networks. The proposed approach trains a neural network model to construct the relationship function among response, inputs and parameters of a dynamic system, which is then used to predict the responses of the system. Three performance functions are developed to evaluate the fitness of the predicted responses. The best parameter settings can be obtained by performing a CACO algorithm according ...
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We develop a Recursive L1-Regularized Least Squares (SPARLS) algorithm for the estimation of a sparse tap-weight vector in the adaptive filtering setting. The SPARLS algorithm exploits noisy observations of the tap-weight vector output stream and produces its estimate using an Expectation-Maximization type algorithm. We prove the convergence of the SPARLS algorithm to a near-optimal estimate in a stationary environment and present analytical results for the steady state error. Simulation studies in the context of channel estimation, employing multi-path wireless channels, show that the SPARLS algorithm has significant improvement over the conventional widely-used Recursive Least Squares (RLS) algorithm in terms of mean squared error (MSE). Moreover, these simulation studies suggest that the SPARLS algorithm (with slight modifications) can operate with lower computational requirements than the RLS algorithm, when applied to tap
Multiple sequence alignment plays an important role in molecular sequence analysis. An alignment is the arrangement of two (pairwise alignment) or more (multiple alignment) sequences of residues (nucleotides or amino acids) that maximizes the similarities between them. Algorithmically, the problem consists of opening and extending gaps in the sequences to maximize an objective function (measurement of similarity). A simple genetic algorithm was developed and implemented in the software MSA-GA. Genetic algorithms, a class of evolutionary algorithms, are well suited for problems of this nature since residues and gaps are discrete units. An evolutionary algorithm cannot compete in terms of speed with progressive alignment methods but it has the advantage of being able to correct for initially misaligned sequences; which is not possible with the progressive method. This was shown using the BaliBase benchmark, where Clustal-W alignments were used to seed the initial population in MSA-GA, improving outcome.
Microarray gene expression data generally suffers from missing value problem due to a variety of experimental reasons. Since the missing data points can adversely affect downstream analysis, many algorithms have been proposed to impute missing values. In this survey, we provide a comprehensive review of existing missing value imputation algorithms, focusing on their underlying algorithmic techniques and how they utilize local or global information from within the data, or their use of domain knowledge during imputation. In addition, we describe how the imputation results can be validated and the different ways to assess the performance of different imputation algorithms, as well as a discussion on some possible future research directions. It is hoped that this review will give the readers a good understanding of the current development in this field and inspire them to come up with the next generation of imputation algorithms ...
This paper presents a series of experiments demonstrating the capacity of single-walled carbon-nanotube (SWCNT)/liquid crystal (LC) mixtures to be trained by evolutionary algorithms to act as classifiers on linear and nonlinear binary datasets. The training process is formulated as an optimisation problem with hardware in the loop. The liquid SWCNT/LC samples used here are un-configured and with nonlinear current-voltage relationship, thus presenting a potential for being evolved. The nature of the problem means that derivative-free stochastic search algorithms are required. Results presented here are based on differential evolution (DE) and particle swarm optimisation (PSO). Further investigations using DE, suggest that a SWCNT/LC material is capable of being reconfigured for different binary classification problems, corroborating previous research. In addition, it is able to retain a physical memory of each of the solutions to the problems it has been trained to solve. ...
This article presents a method for segmenting and classifying edges using minimum description length (MDL) approximation with automatically generated break points. A scheme is proposed where junction candidates are first detected in a multiscale preprocessing step, which generates junction candidates with associated regions of interest. These junction features are matched to edges based on spatial coincidence. For each matched pair, a tentative break point is introduced at the edge point closest to the junction. Finally, these feature combinations serve as input for an MDL approximation method which tests the validity of the break point hypotheses and classifies the resulting edge segments as either "straight" or "curved." Experiments on real world image data demonstrate the viability of the approach.. ...
For any given optimization problem, it is a good idea to compare several of the available algorithms that are applicable to that problem-in general, one often finds that the "best" algorithm strongly depends upon the problem at hand. However, comparing algorithms requires a little bit of care because the function-value/parameter tolerance tests are not all implemented in exactly the same way for different algorithms. So, for example, the same fractional 10−4 tolerance on the function value might produce a much more accurate minimum in one algorithm compared to another, and matching them might require some experimentation with the tolerances. Instead, a more fair and reliable way to compare two different algorithms is to run one until the function value is converged to some value fA, and then run the second algorithm with the minf_max termination test set to minf_max=fA. That is, ask how long it takes for the two algorithms to reach the same function value. Better yet, run some algorithm for a ...
For any given optimization problem, it is a good idea to compare several of the available algorithms that are applicable to that problem-in general, one often finds that the "best" algorithm strongly depends upon the problem at hand. However, comparing algorithms requires a little bit of care because the function-value/parameter tolerance tests are not all implemented in exactly the same way for different algorithms. So, for example, the same fractional 10−4 tolerance on the function value might produce a much more accurate minimum in one algorithm compared to another, and matching them might require some experimentation with the tolerances. Instead, a more fair and reliable way to compare two different algorithms is to run one until the function value is converged to some value fA, and then run the second algorithm with the minf_max termination test set to minf_max=fA. That is, ask how long it takes for the two algorithms to reach the same function value. Better yet, run some algorithm for a ...
Valiants theory of holographic algorithms is a new design method to produce polynomial time algorithms. Information is represented in a superposition of linear vectors in a holographic mix. This mixture creates the possibility for exponential sized cancellations of fragments of local computations. The underlying computation is done by invoking the Fisher-Kasteleyn-Temperley method for counting perfect matchings for planar graphs, which uses Pfaffians and runs in polynomial time. In this way some seemingly exponential time computations can be done in polynomial time, and some minor variations of the problems are known to be NP-hard or #P-hard. Holographic algorithms challenge our conception of what polynomial time computations can do, in view of the P vs. NP question. In this talk we will survey some new developments in holographic algorithms. ...
A large-scale multiple surveillance system for infectious disease outbreaks has been in operation in England and Wales since the early 1990s. Changes to the statistical algorithm at the heart of the system were proposed and the purpose of this paper is to compare two new algorithms with the original algorithm. Test data to evaluate performance are created from weekly counts of the number of cases of each of more than 2000 diseases over a twenty-year period. The time series of each disease is separated into one series giving the baseline (background) disease incidence and a second series giving disease outbreaks. One series is shifted forward by twelve months and the two are then recombined, giving a realistic series in which it is known where outbreaks have been added. The metrics used to evaluate performance include a scoring rule that appropriately balances sensitivity against specificity and is sensitive to variation in probabilities near 1. In the context of disease surveillance, a scoring rule can
This report approaches the question of multi-objective optimization for optimum shape design in aerodynamics. The employed optimizer is a semi-stochas- tic method, more precisely a Genetic Algorithm (GA). GAs are very robust optimization algorithms particularly well suited for problems in which (1) the initialization is not intuitive, (2) the parameters to be optimized are not all of the same type (boolean, integer, real, functionnal), (3) the cost functional may present several local minima, (4) several criteria should be accounted for simultaneously (multiphysics, efficiency, cost, quality, ...). In a multi-objective optimization problem, there is no unique optimal solution but a whole set of potential solutions since in general no solution is optimal w.r.t. all criteria simultaneously ; instead, one identifies a set of non-dominated solutions, referred to as the Pareto optimal front. After making these concepts precise, genetic algorithms are implemented and first tested on academic examples ; then a
... (GA) are a computational paradigm inspired by the mechanics of natural evolution, including survival of the fittest, reproduction, and mutation. Surprisingly, these mechanics can be used to solve (i.e. compute) a wide range of practical problems, including numeric problems. Concrete examples illustrate how to encode a problem for solution as a genetic algorithm, and help explain why genetic algorithms work. Genetic algorithms are a popular line of current research, and there are many references describing both the theory of genetic algorithms and their use in practical problem solving ...
SECOND CALL FOR PAPERS ====================== Journal of Combinatorial Optimization Special Issue on Computational Molecular Biology Guest Editors: Ying Xu, Satoru Miyano, Tom Head. Submission Deadline: August 15, 1998. The past ten years have witnessed the rapid development of a new discipline, computational molecular biology. Combinatorial optimization and algorithms have played a significant role in advancing this new discipline. The partnership between mathematics, in particular combinatorial optimization and algorithms, and molecular biology has greatly enriched both fields, leading to new ways of thinking and greater challenges to meet. The scope of this Special Issue includes all aspects of combinatorial optimization and algorithms in computational molecular biology. Original papers are solicited that describe research on combinatorial methods for problems arising from the following areas (nonexhaustive) of molecular biology: -- DNA sequencing -- DNA mapping -- recognition of genes and ...
Feature Selection is central to modern data science, from exploratory data analysis to predictive model-building. The â stabilityâ of a feature selection algorithm refers to the robustness of its feature preferences, with respect to data sampling and to its stochastic nature. An algorithm is `unstable if a small change in data leads to large changes in the chosen feature subset. Whilst the idea is simple, quantifying this has proven more challenging---we note numerous proposals in the literature, each with different motivation and justification. We present a rigorous statistical treatment for this issue. In particular, with this work we consolidate the literature and provide (1) a deeper understanding of existing work based on a small set of properties, and (2) a clearly justified statistical approach with several novel benefits. This approach serves to identify a stability measure obeying all desirable properties, and (for the first time in the literature) allowing confidence intervals and ...
This is the fourth course in the computer science sequence, building upon the concepts and skills acquired in the first three. Whereas CSC 221 and CSC 222 focused on the design of simple algorithms and CSC 321 focused on basic data structures, this course considers both facets of problem solving and their interrelationships. In order to solve complex problems efficiently, it is necessary to design algorithms and data structures together since the data structure is motivated by and affects the algorithm that accesses it. As the name of the course suggests, special attention will be paid to analyzing the efficiency of specific algorithms, and how the appropriate data structure can affect efficiency. Specific topics covered in this course will include: advanced data structures (e.g., trees, graphs and hash tables), common algorithms and their efficiency (e.g., binary search, heapsort, graph traversal, and big-Oh analysis), and problem-solving approaches (e.g., divide-and-conquer, backtracking, and ...
In computer science, the analysis of algorithms is the determination of the computational complexity of algorithms, that is the amount of time, storage and/or other resources necessary to execute them. Usually, this involves determining a function that relates the length of an algorithms input to the number of steps it takes (its time complexity) or the number of storage locations it uses (its space complexity). An algorithm is said to be efficient when this functions values are small, or grow slowly compared to a growth in the size of the input. Different inputs of the same length may cause the algorithm to have different behavior, so best, worst and average case descriptions might all be of practical interest. When not otherwise specified, the function describing the performance of an algorithm is usually an upper bound, determined from the worst case inputs to the algorithm. The term "analysis of algorithms" was coined by Donald Knuth.[1] Algorithm analysis is an important part of a broader ...
Finding the minimum energy amino acid side-chain conformation is a fundamental problem in both homology modeling and protein design. To address this issue, numerous computational algorithms have been proposed. However, there have been few quantitative comparisons between methods and there is very little general understanding of the types of problems that are appropriate for each algorithm. Here, we study four common search techniques: Monte Carlo (MC) and Monte Carlo plus quench (MCQ); genetic algorithms (GA); self-consistent mean field (SCMF); and dead-end elimination (DEE). Both SCMF and DEE are deterministic, and if DEE converges, it is guaranteed that its solution is the global minimum energy conformation (GMEC). This provides a means to compare the accuracy of SCMF and the stochastic methods. For the side-chain placement calculations, we find that DEE rapidly converges to the GMEC in all the test cases. The other algorithms converge on significantly incorrect solutions; the average fraction ...
A friendly and accessible introduction to the most useful algorithms Computer algorithms are the basic recipes for programming. Professional programmers need to know how to use algorithms to solve difficult programming problems. Written in simple, intuitive English, this book describes how and when to use the most practical classic algorithms, and even how to create new algorithms to meet future needs. The book also includes a collection of questions that can help readers prepare for a programming job interview. Reveals methods for manipulating common data structures such as arrays, linked lists, trees, and networks Addresses advanced data structures such as heaps, 2-3 trees, B-trees Addresses general problem-solving techniques such as branch and bound, divide and conquer, recursion, backtracking, heuristics, and more Reviews sorting and searching, network algorithms, and numerical algorithms Includes general problem-solving techniques such as brute force and exhaustive search, divide and ...
techreport{GaMi87tr, author="Hillel Gazit and Gary L. Miller", title="A Parallel Algorithm for Finding a Separator in Planar Graphs", institution="University of Southern California", year="1987", address="Los Angeles", number="CRI 87-54", bib2html_rescat = {Parallel Algorithms,Graph Separators,Planar Graph Algorithms}, thanks="NSF DCR-8514961 ...
DESCRIPTION. MDBLOCKS( Minimum Description length method for Haplotype BLOCKS) uses the mimimum description length model to delineate haplotype blocks. ...
Synonyms for diagnostic algorithm in Free Thesaurus. Antonyms for diagnostic algorithm. 2 synonyms for algorithm: algorithmic program, algorithmic rule. What are synonyms for diagnostic algorithm?
This book consists of methodological contributions on different scenarios of experimental analysis. The first part overviews the main issues in the experimental analysis of algorithms, and discusses the experimental cycle of algorithm development; the second part treats the characterization by means of statistical distributions of algorithm performance in terms of solution quality, runtime and other measures; and the third part collects advanced methods from experimental design for configuring and tuning algorithms on a specific class of instances with the goal of using the least amount of experimentation. The contributor list includes leading scientists in algorithm design, statistical design, optimization and heuristics, and most chapters provide theoretical background and are enriched with case studies ...
This is a multi-part message in MIME format. ------=_NextPart_000_0000_01C5B91F.83FE4110 Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: 7bit Hi I have been working with ARToolkit for a few years by now and I think it is a terrific tool. However I still dont understand the core of ARToolkit - namely the image analysis. I hope that it will be possible to modify the algorithm so that the user can cover a part of the outer black square and still be able to recognize the marker. It should be possible to do but I dont understand in detail what goes on in the algorithm since there are no comments in the code and the C code seems to have been written to be super efficient rather than easy to read (I hope I do not offend anyone by assuming this). Does anyone have any detailed information about the image analysis algorithm? Alternatively we might have to write a new algorithm from scratch. Do you know any books or other sources that gives a good explanation on how an image ...
This paper proposes a Grammatical Evolution framework to the automatic design of Evolutionary Algorithms. We define a grammar that has the ability to combine components regularly appearing in existing evolutionary algorithms, aiming to achieve novel and fully functional optimization methods. The problem of the Royal Road Functions is used to assess the capacity of the framework to evolve algorithms. Results show that the computational system is able to evolve simple evolutionary algorithms that can effectively solve Royal Road instances. Moreover, some unusual design solutions, competitive with standard approaches, are also proposed by the grammatical evolution framework ...
Greedy motif searching Developed by Gerald Hertz and Gary Stormo in 1989 CONSENSUS is the tool based on greedy algorithm Faster than Brute force and Simple motif search algorithms An approximation algorithm with an unknown approximation ratio
Mechanisms for adapting models, filters, decisions, regulators, and so on to changing properties of a system or a signal are of fundamental importance in many modern signal processing and control algorithms. This contribution describes a basic foundation for developing and analyzing such algorithms. Special attention is paid to the rationale behind the different algorithms, thus distinguishing between "optimal" algorithms and "ad hoc" algorithms. We also outline the basic approaches to performance analysis of adaptive algorithms.. ...
We consider the problem of learning an unknown large-margin halfspace in the context of parallel computation, giving both positive and negative results. As our main positive result, we give a parallel algorithm for learning a large-margin halfspace, based on an algorithm of Nesterovs that performs gradient descent with a momentum term. We show that this algorithm can learn an unknown $\gamma$-margin halfspace over $n$ dimensions using $n \cdot \text{poly}(1/\gamma)$ processors and running in time $\tilde{O}(1/\gamma)+O(\log n)$. In contrast, naive parallel algorithms that learn a $\gamma$-margin halfspace in time that depends polylogarithmically on $n$ have an inverse quadratic running time dependence on the margin parameter $\gamma$. Our negative result deals with boosting, which is a standard approach to learning large-margin halfspaces. We prove that in the original PAC framework, in which a weak learning algorithm is provided as an oracle that is called by the booster, boosting cannot be ...
In 1950, Alan Turing proposed a "learning machine" which would parallel the principles of evolution.[8] Computer simulation of evolution started as early as in 1954 with the work of Nils Aall Barricelli, who was using the computer at the Institute for Advanced Study in Princeton, New Jersey.[9][10] His 1954 publication was not widely noticed. Starting in 1957,[11] the Australian quantitative geneticist Alex Fraser published a series of papers on simulation of artificial selection of organisms with multiple loci controlling a measurable trait. From these beginnings, computer simulation of evolution by biologists became more common in the early 1960s, and the methods were described in books by Fraser and Burnell (1970)[12] and Crosby (1973).[13] Frasers simulations included all of the essential elements of modern genetic algorithms. In addition, Hans-Joachim Bremermann published a series of papers in the 1960s that also adopted a population of solution to optimization problems, undergoing ...
If you have a question about this talk, please contact Matthew Ireland.. The talk will consist of talking about different methods for mainly 2D maze generation and solving algorithms. We will discuss the difference of space, time complexity, possible implementation difficulties arising in each algorithm and results (the algorithm finding any path or the shortest path). We will also briefly touch upon which of the algorithms are human usable. The algorithms mentioned will include dead-end filling, A* algorithm, etc for maze generating algorithms and Kruskal, Hunt-and-Kill, Sidewinder, etc for maze solving algorithms. In addition, we will see what changes can be made in order to have a more attractive maze for the human eye. Lastly, we will look how with the help of matrices it is possible to expand these algorithms into higher dimensions (3D or higher).. This talk is part of the Churchill CompSci Talks series.. ...
Detecting communities, and labeling nodes, is a ubiquitous problem in the study of networks. Recently, we developed scalable Belief Propagation algorithms that update probability distributions of node labels until they reach a fixed point. In addition to being of practical use, these algorithms can be studied analytically, revealing phase transitions in the ability of any algorithm to solve this problem. Specifically, there is a detectability transition in the stochastic block model, below which no algorithm can label nodes better than chance. This transition was subsequently established rigorously by Mossel, Neeman, and Sly, and Massoulie.Ill explain this transition, and give an accessible introduction to Belief Propagation and the analogy with free energy and the cavity method of statistical physics. Well see that the consensus of many good solutions is a better labeling than the best solution --- something that is true for many real-world optimization problems. While many algorithms ...
Abstract: Global routing in VLSI (very large scale integration) design is one of the most challenging discrete optimization problems in computational theory and practice. In this paper, we present a polynomial time algorithm for the global routing problem based on integer programming formulation with a theoretical approximation bound. The algorithm ensures that all routing demands are satisfied concurrently, and the overall cost is approximately minimized. We provide both serial and parallel implementation as well as develop several heuristics used to improve the quality of the solution and reduce running time. We provide computational results on two sets of well-known benchmarks and show that, with a certain set of heuristics, our new algorithms perform extremely well compared with other integer-programming models. Keywords: Global routing in VLSI design, Approximation algorithms, Integer programming model Category 1: Applications -- Science and Engineering (VLSI layout ). Category 2: Integer ...
In many data analysis tasks, one is often confronted with very high dimensional data. The feature selection problem is essentially a combinatorial optimization problem which is computationally expensive. To overcome this problem it is frequently assumed either that features independently influence the class variable or do so only involving pairwise feature interaction. In prior work [18], we have explained the use of a new measure called multidimensional interaction information (MII) for feature selection. The advantage of MII is that it can consider third or higher order feature interaction. Using dominant set clustering, we can extract most of the informative features in the leading dominant sets in advance, limiting the search space for higher order interactions. In this paper, we provide a comparison of different similarity measures based on mutual information. Experimental results demonstrate the effectiveness of our feature selection method on a number of standard data-sets.
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently -- those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed--memory parallel machine which allows for message--passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers -- the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current
There looks a download Convex Optimization in of the Burgundy life of reverse France, that discusses five scholarly campaigns. In this treatment we are of the Impressionism parts that do up the world, and we understand of the unds blau and how data and null processes resolved Then of the time requirements thou again. prize miles, cite-to-doi and seeds start published in this cultural m of evil domains. easily the cookies fold issues black as Close De Vougeot, Chevaliers Du Tastevin and Hill of Corton. years of s phrases electrical to Monthelie, Volnay and Pommard have the forerunners, and at the click of the NAVC the Studies weep also colored by swimsuits, tools and the palms of agencies they are. But soon all ships have monitored: that would relay ever-growing more blocks. download Convex Optimization in Normed Spaces: Theory,, and going a m in article, done on fury. All boasting first-author-surname for buyers will Learn called by the download Convex Optimization in Normed Spaces: at the ...
Consider algorithms for sequentially placing a coin each day either in a heads or a tails configuration depending on how coins were placed on past days. For instance, the rule might say that if you placed heads yesterday, today you place tails, and if yesterday you placed tails, this time you place heads. The algorithm might depend on the date, too: maybe on Wednesdays you place heads if and only if you placed tails last Wednesday, but on all other days of the week you place heads.. Heres an interesting question about a coin-placing algorithm: Is it mathematically coherent to suppose that the algorithm had been running from eternity? For some algorithms, the answer is positive. Both of the algorithms I described above have that property. But not all algorithms are like that. For instance, heres an algorithm based on a comment by Ian: if infinitely many heads have been placed, place tails; otherwise, place heads. This algorithm could not have been running from eternity. [Proof: For suppose it ...
Programmatic Marketing Platform Employs Data Science to Drive Business Goals with Unprecedented Choice and Flexibility Boston - May 22, 2013 - DataXu today introduced the industrys first Algorithm Marketplace, a major new addition to its enterprise programmatic marketing platform that leverages data science to increase the efficiency and effectiveness of digital advertising. The Marketplace is a library of algorithms created over time from DataXus experience solving clients complex marketing problems. For the first time, dozens of algorithms are available to users in one place, so brands and their agencies get complete transparency and control of their advertising investment strategies. Marketers can also continue to innovate and collaborate with DataXu to develop custom algorithms that address their business unique opportunities and challenges.. "The Algorithm Marketplace helps brands drive sales through data science," said Mike Baker, CEO of DataXu. "Weve worked with our clients to solve ...
CMfinder is a new tool to predict RNA motifs in unaligned sequences. It is an expectation maximization algorithm using covariance models for motif description, featuring novel integration of multiple techniques for effective search of motif space, and a Bayesian framework that blends mutual informat …
Brief Course Description This course introduces basic tools and techniques for the design and analysis of computer algorithms. Topics include asymptotic notations and analysis, greedy algorithms, divide and conquer, dynamic programming, linear programming, network flows, NP-completeness, approximation algorithms, and randomized algorithms. For each topic, beside in-depth coverage, one or more representative problems and their algorithms shall be discussed.. In addition to the design and analysis of algorithms, students are expected to gain substantial discrete mathematics problem solving skills essential for computer engineers.. ...
EEG data contains high-dimensional data that requires considerable computational power for distinguishing different classes. Dimension reduction is commonly used to reduces the necessary training time of the classifiers with some degree of accuracy lost. The dimension reduction is usually performed on either feature or electrode space. In this study, a new dimension reduction method that reduce the number of electrodes and features using variations of Particle Swarm Optimization (PSO) is used. The variation is in terms of parameter adjustment and adding a mutation operator to the PSO. The results are assessed based on the dimension reduction percentage, the potential of selected electrodes and the degree of performance lost. An Extreme Learning Machine (ELM) is used as the primary classifier to evaluate the sets of electrodes and features selected by PSO. Two alternative classifiers such as Polynomial SVM and Perceptron are used for further evaluation of the reduced dimension data. The results indicate
The Flow Set with Partial Order - The flow set with partial order is a mixed-integer set described by a budget on total flow and a partial order on the arcs that may carry positive flow. This set is a common substructure of resource allocation and scheduling problems with precedence constraints and robust network flow problems under demand/capacity uncertainty. We give a polyhedral analysis of the convex hull of the flow set with partial order. Unlike for the flow set without partial order, cover-type inequalities based on partial order structure are a function of a lifting sequence. We study the lifting sequences and describe structural results on the lifting coefficients for general and simpler special cases. We show that all lifting coefficients can be computed in polynomial time by solving maximum weight closure problems in general. For the special case of induced-minimal covers, we give a sequencedependent characterization of the lifting coefficients. We prove, however, if the partial order is
This paper presents a method of applying particle swarm optimization (PSO) algorithm to a flow shop scheduling problem. Permutation encoding of job indices
We discuss the approach to the analysis of learning algorithms that we have taken in our laboratory and summarize the results we have obtained in the last few years. We have worked on refining and generalizing the PAC learning model introduced by Valiant. Measures of performance for learning algorithms that we have examined include computational complexity, sample complexity, probability of misclassification (learning curves), and worst case total number of misclassifications or hypothesis updates. We have looked for theoretically optimal bounds on these performance measures, and for learning algorithms that achieve these bounds. Learning problems we have examined include those for decision trees, neural networks, finite automata, conjunctive concepts on structural domains, and various classes of Boolean functions. We also worked on clustering data represented as sequences over a finite alphabet. Many of the new learning algorithms that we have developed have been tested empirically as well.*ALGORITHMS
In this research, we bridge algorithm and system design environments creating a unified design flow facilitating algorithm and system co-design. It enables algorithm realizations over heterogeneous platforms, while still tuning the algorithm according to platform needs. Our design flow starts with algorithm design in Simulink, out of which a System Level Design Language (SLDL)-based specification is synthesized. This specification then is used for design space exploration across heterogeneous target platforms and abstraction levels, and, after identifying a suitable platform, synthesized to HW/SW implementations. It realizes a unified development cycle across algorithm modeling and system-level design with quick responses to design decisions on algorithm-, specification- and system exploration level. It empowers the designer to combine analysis results across environments, apply cross layer optimizations, which will yield an overall optimized design through rapid design iterations. synthesize ...
A nonparametric deconvolution algorithm for recovering the photon time-of-flight distribution (TOFD) from time-resolved (TR) measurements is described. The algorithm combines wavelet denoising and a two-stage deconvolution method based on generalized singular value decomposition and Tikhonov regularization. The efficacy of the algorithm was tested on simulated and experimental TR data and the results show that it can recover the photon TOFD with high fidelity. Combined with the microscopic Beer-Lambert law, the algorithm enables accurate quantification of absorption changes from arbitrary time-of-flight windows, thereby optimizing the depth sensitivity provided by TR measurements.. ©2012 Optical Society of America. Full Article , PDF Article ...
Disclosed is a technique for classifying tissue based on image data. A plurality of tissue parameters are extracted from image data (e.g., magnetic resonance image data) to be classified. The parameters are preprocessed, and the tissue is classified using a classification algorithm and the preprocessed parameters. In one embodiment, the parameters are preprocessed by discretization of the parameters. The classification algorithm may use a decision model for the classification of the tissue, and the decision model may be generated by performing a machine learning algorithm using preprocessed tissue parameters in a training set of data. In one embodiment, the machine learning algorithm generates a Bayesian network. The image data used may be magnetic resonance image data that was obtained before and after the intravenous administration of lymphotropic superparamagnetic nanoparticles.
The first linear-time suffix tree algorithm was developed by Weiner in 1973. A more space efficient algorithm was produced by McCreight in 1976, and Ukkonen produced an "on-line" variant of it in 1995. The key to search speed in a suffix tree is that there is a path from the root for each suffix of the text. This means that at most n comparisons are needed to find a pattern of length n. Lloyd Allison has a detailed introduction to suffix trees, which includes a javascript suffix tree demonstration and a discussion of suffix tree applications. His example uses the string mississippi, which can be decomposed into 12 suffixes (Fig 1). A suffix is a substring that includes the final character of the string, for instance the suffix ippi can be found starting at position 8.. A suffix tree can be either implicit (Fig 2a) or explicit (Fig 2b). Suffixes in an implicit suffix tree can end at an interior node -- making them prefixes of another suffix. For example, in the implicit suffix tree for ...
Title: A random perturbation approach to some stochastic approximation algorithms in optimization Abstract: Many large-scale learning problems in modern statistics and machine learning can be reduced to solving stochastic optimization problems, i.e., the search for (local) minimum points of the expectation of an objective random function (loss function). These optimization problems are usually solved by certain stochastic approximation algorithms, which are recursive update rules with random inputs in each iteration. In this talk, we will be considering various types of such stochastic approximation algorithms, including the stochastic gradient descent, the stochastic composite gradient descent, as well as the stochastic heavy-ball method. By introducing approximating diffusion processes to the discrete recursive schemes, we will analyze the convergence of the diffusion limits to these algorithms via delicate techniques in stochastic analysis and asymptotic methods, in particular random ...