Learn about algorithms, programming, and abstraction in computing. Explore the definition and properties of algorithms, the relationship between algorithms and programming, and the concept of abstraction. Discover how algorithms are like recipes and how abstraction simplifies complex tasks in comput

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Near-Optimal Quantum Algorithms for String Problems by Ce Jin and Shyan Akmal presents groundbreaking research on string problem solutions using quantum algorithms. The study delves into various key topics such as Combinatorial Pattern Matching, Basic String Problems, Quantum Black-box Model, and mo

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Approximation algorithms aim to find near-optimal solutions for optimization problems, with the performance ratio indicating how close the algorithm's solution is to the optimal solution. The terminology used in approximation algorithms includes P (optimization problem), C (approximation algorithm),

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Delve into the fascinating world of the particle-on-a-ring approximation in chemistry, exploring concepts like quantum quantization of energy levels, De Broglie approach, Schrödinger equation, and its relevance to the electronic structure of molecules. Discover how confining particles to a ring lea

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This research explores primal-dual algorithms for node-weighted network design in planar graphs, focusing on feedback vertex set problems, flavors and toppings of FVS, FVS in general graphs, and FVS in planar graphs. The study delves into NP-hard problems, approximation algorithms, and previous rela

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In this lecture, Kasey Champion covers a wide range of topics including graph algorithms, data structures, coding projects, and important midterm topics for CSE 373. The lecture emphasizes understanding ADTs, data structures, asymptotic analysis, sorting algorithms, memory management, P vs. NP, heap

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Randomized algorithms incorporate randomness into computations, with Las Vegas algorithms always providing the correct answer but varying in time, while Monte Carlo algorithms occasionally give wrong answers. Quick Sort is a classic Las Vegas algorithm that involves pivoting elements for sorting. Ch

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Explore the fundamental concepts of algorithms and programming through visual representations and practical examples. Learn about algorithmic thinking, abstraction, recipe-like algorithms, and the importance of logical steps in accomplishing tasks. Discover how algorithms encapsulate data and instru

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Distributed algorithms play a crucial role in leader election within anonymous systems where nodes lack unique identifiers. The content discusses the challenges and impossibility results of deterministic leader election in such systems. It explains synchronous and asynchronous distributed algorithms

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Explore the mathematical analysis of algorithms in CMPE371 for Fall 2023-2024, focusing on non-recursive and recursive algorithms. Learn how to analyze non-recursive algorithms by deciding on input size parameters, identifying basic operations, and simplifying summations. Dive into recursive algorit

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Exploring learning-based low-rank approximations and linear sketches in matrices, including techniques like dimensionality reduction, regression, and streaming algorithms. Discusses the use of random matrices, sparse matrices, and the concept of low-rank approximation through singular value decompos

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Grigory Yaroslavtsev and collaborators present an improved approximation for the Directed Spanner Problem, exploring the concept of k-Spanner in directed graphs. The research delves into finding the sparsest k-spanner, preserving distances and discussing applications, including simulating synchroniz

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Structural components subjected to multi-axial cyclic loading can be analyzed for low-cycle fatigue using notch approximation. By transforming elastic response into an elastoplastic state, the computation time is reduced, and fatigue evaluation is done based on the Smith-Watson-Topper model. Strain-

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Pseudodeterministic algorithms provide a unique approach to the search problem associated with binary relations, offering an error reduction technique while sacrificing the ability to approximate the average value of a function. By introducing m-pseudodeterministic and pseudo-pseudodeterministic alg

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This paper proposes a method for dimensionality reduction based on functional approximation using Gaussian basis functions. Nonlinear Gauss weights are utilized to train a least squares support vector machine (LS-SVM) model, with further variable selection using forward-backward methodology. The met

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This piece discusses approximation algorithms for stochastic optimization problems, focusing on modeling uncertainty in inputs, adapting to stochastic predictions, and exploring different optimization themes. It covers topics such as weakening the adversary in online stochastic optimization, two-sta

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Explore the world of STL algorithms through an insightful discussion on the definition of algorithms, the advantages of using STL algorithms over raw loops, and the different classes of STL algorithms available. Discover how these pre-built libraries can enhance your programming efficiency and code

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Push beyond traditional NLP models like logistic regression and PCFG with approximation-aware training. Explore factor graphs, BP algorithm, and fancier models to improve predictions. Learn how to tweak algorithms, tune parameters, and build custom models for machine learning in NLP.

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Delve into the world of algorithms in game design, from understanding the fundamental concept of algorithms to their pervasive presence in various aspects of gaming, such as military simulations, medical simulations, and gameplay mechanics. Explore how algorithms shape experiences in different types

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Explore the world of evolutionary computation and genetic algorithms through a presentation outlining the concepts of genetic algorithms, parallel genetic algorithms, genetic programming, evolution strategies, classifier systems, and evolution programming. Delve into scenarios in the forest where gi

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ACCEPT is an Approximate C Compiler framework that allows programmers to designate which parts of the code can be approximated for energy and performance trade-offs. It automatically determines the best approximation parameters, identifies safe approximation areas, and can utilize FPGA for hardware

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Regret-bounded vehicle routing problems aim to minimize client delays by considering client-centric views and bounded client regret measures. This involves measuring waiting times relative to shortest-path distances from the starting depot. Additive and multiplicative regret measures are used to add

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This research explores regret-bounded vehicle routing problems (VRPs) where the focus is on minimizing client delays based on their distances from the starting depot. The study introduces a client-centric view to measure regret and devises algorithms for both additive and multiplicative regret-based

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Explore the world of online advertisements and algorithms through insightful discussions on online advertising, modern developments in online algorithms, and practical optimization strategies like budgeted allocation. Delve into topics such as decision-making under uncertainty, accessing algorithms,

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Mitigating memory and bandwidth walls, this research extends rollback-free value prediction to GPUs, achieving up to 2x improvement in energy and performance while maintaining 10% quality degradation. Utilizing microarchitecturally-triggered approximation to predict missed loads, this work focuses o

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This comprehensive guide explores the implementation of iterative algorithms with SPARQL, focusing on YarcData/Cray's approach to using these algorithms. It covers YarcData's interest in graphs, the Urika appliance, iterative algorithms in machine learning, implementation approach, and algorithms im

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Sorting algorithms play a crucial role in computer science and computing tasks, consuming a significant portion of computing power. Various algorithms such as Bubble Sort, Selection Sort, and Insertion Sort are discussed for sorting a list of values efficiently. Quadratic sorting algorithms like Sel

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This research presents LP-Based Algorithms for the Capacitated Facility Location problem, aiming to choose facilities to open and assign clients to these facilities efficiently. It discusses solving the problem using metric costs, client and facility sets, capacities, and opening costs. The research

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Centralized sublinear algorithms and their relation to distributed computing are explored, emphasizing the efficiency of algorithms in processing large inputs in sublinear time. Examples of sublinear algorithms for various objects are provided, along with the computation and approximation of graph p

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The content covers iterative root approximation using natural logarithm in solving equations. It explores finding roots by iterative formulas and demonstrates calculations to reach approximate values. The process involves selecting intervals to show correct values and ensuring continuity for accurat

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Explore testable bounded degree graph properties, sparse graphs, d-bounded degree graphs, hyperfinite graphs, arboricity, maximum matching algorithms, and sublinear time approximation algorithms in graph data streams. Learn about various graph models and properties with examples, showcasing the impo

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This review covers essential topics from the CS260 Parallel Algorithms course by Yihan Sun, focusing on key concepts such as scheduler programs, cost models, reduce and scan techniques, PRAM models, atomic primitives, small algorithms, the master theorem, and sorting algorithms like Quicksort and Me

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This research discusses hierarchy-based algorithms for minimizing makespan in scheduling problems with precedence and communication constraints. Various approximation techniques, open questions in scheduling theory, and QPTAS for different settings are explored, including the possibility of beating

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Stochastic algorithms, including Monte Carlo and Las Vegas variations, leverage randomness to tackle complex tasks efficiently. While Monte Carlo algorithms prioritize speed with some margin of error, Las Vegas algorithms guarantee accuracy but with variable runtime. They play a vital role in primal

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Explore correlation clustering, a powerful clustering method using qualitative similarities. Learn about LP rounding techniques, approximation algorithms, NP-hardness, and practical applications like document deduplication. Discover insights from leading researchers and tutorials on theory and pract

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This content covers a lecture on a randomized algorithm for finding an approximate median element using elementary probability theory. It discusses the importance of insight and basic probability in designing and analyzing such algorithms. The lecture presents a simple probability exercise involving

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This research explores the use of quasi-interpolation techniques to approximate functions from scattered data points in high dimensions. It discusses the interpretation of Moving Least Squares (MLS) for direct pointwise approximation of differential operators, handling singularities, and improving a

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Explore characteristics and properties of good approximation operators, such as quasi-interpolation and Moving Least-Squares (MLS), for approximating functions with singularities and near boundaries. Learn about direct approximation of local functionals and high-order approximation methods for non-s

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Illustration of lock-free and wait-free algorithms compared to blocking algorithms, with insights on concurrent object execution, blocking vs. non-blocking algorithms, definitions, comparisons between locks, lock-free, and wait-free approaches, and explanations on making algorithms wait-free. Exampl

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The research discusses LP-based approximation algorithms for solving Multi-Vehicle Minimum Latency Problems, focusing on minimizing waiting times for vehicles visiting clients starting from a depot. Various cases, including single- and multi-depot scenarios, are explored, and significant improvement

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