Welfare maximization refers to an economic principle or objective that aims to achieve the highest possible level of overall welfare or well-being for individuals within a society. This concept is often used in the context of public policy, economics, and social welfare programs, where the goal is to allocate resources in a way that maximizes the utility or happiness of the population.
Shizuka Ōya is a versatile Japanese actress, voice actress, and singer, known for her work in anime, television dramas, and films. She gained popularity for voicing several notable characters in anime series, contributing significantly to the voice acting industry in Japan. Additionally, her talents extend to music, where she has released songs related to her roles in various media.
Genetic algorithms (GAs) are a type of optimization and search technique inspired by the principles of natural selection and genetics. In the context of economics, genetic algorithms are used to solve complex problems involving optimization, simulation, and decision-making. ### Key Concepts of Genetic Algorithms: 1. **Population**: A GA begins with a group of potential solutions to a problem, known as the population. Each individual in this population represents a possible solution.
Genetic improvement in computer science refers to the use of genetic algorithms and evolutionary computation techniques to enhance and optimize existing software systems. This process leverages principles of natural selection and genetics to improve various attributes of software, such as performance, efficiency, maintainability, or reliability. Here's a breakdown of how genetic improvement typically works: 1. **Representation**: Software programs or their components are represented as individuals in a population.
Gradient descent is an optimization algorithm used to minimize a function by iteratively moving towards the steepest descent direction, which is indicated by the negative gradient of the function. It is widely used in machine learning and deep learning to minimize loss functions during the training of models.
Graduated optimization is a computational technique used primarily in the context of optimization and machine learning, particularly for solving complex problems that may be non-convex or have multiple local minima. The general idea behind graduated optimization is to gradually transform a difficult optimization problem into a simpler one, which can be solved more easily.
The Great Deluge algorithm is a metaheuristic optimization technique inspired by the concept of a flood or deluge used to manage and explore search spaces. It is particularly useful for solving combinatorial optimization problems, where the goal is to find the best solution from a finite set of possible solutions. ### Key Concepts: 1. **Search Space**: The algorithm navigates through a potential solution space, similar to how water would rise and cover terrain, altering the landscape of possible solutions.
Guided Local Search (GLS) is a heuristic search algorithm designed to improve the performance of local search methods for combinatorial optimization problems. It builds upon traditional local search techniques, which often become stuck in local optima, by incorporating additional mechanisms to escape these local minima and thereby explore the solution space more effectively. ### Key Features of Guided Local Search: 1. **Penalty Function**: GLS uses a penalty mechanism that discourages the algorithm from revisiting certain solutions that have previously been explored.
Guillotine cutting refers to a method of cutting materials using a guillotine-style blade, which typically consists of a sharp, straight-edged blade that descends vertically to shear material placed beneath it. This technique is commonly used in various industries for cutting paper, cardboard, plastics, and even certain types of metals. In a printing or publishing context, guillotine cutters are often used for trimming large stacks of paper or printed materials to specific sizes.
A Guillotine partition refers to a method of dividing a geometric space, commonly used in computational geometry, optimization, and various applications such as packing problems and resource allocation. The term is often associated with the partitioning of a rectangular area into smaller rectangles using a series of straight cuts, resembling the action of a guillotine. In a Guillotine partition, the cuts are made either vertically or horizontally, and each cut subdivides the current region into two smaller rectangles.
HiGHS is an open-source optimization solver designed for solving large-scale linear programming (LP) and mixed-integer programming (MIP) problems. Developed as part of the HiGHS project, it focuses on providing efficient algorithms and implementations tailored for high performance in computational optimization tasks. Some key features of HiGHS include: 1. **Efficiency**: HiGHS is optimized for speed and memory usage, making it suitable for handling large problems with many variables and constraints.
Zadeh's rule refers to a concept in fuzzy logic developed by Lotfi Zadeh, who is known as the father of fuzzy set theory. While Zadeh himself did not specifically codify a "Zadeh's rule," the term is often associated with a fundamental principle in fuzzy logic related to the combination of fuzzy sets and the reasoning process within this framework.
Siân Lloyd is a Welsh television presenter and meteorologist, best known for her work as a weather presenter for various UK television networks, including ITV. She gained popularity for her engaging on-screen presence and has contributed to programs related to weather forecasting and environmental issues. In addition to her broadcasting career, she has a background in geography and has worked in both journalism and television production. Lloyd is also known for her advocacy on issues related to climate change and public awareness of weather impacts.
IOSO may refer to different things based on the context, but one common reference is to a type of optimization software. IOSO is a numerical optimization tool that uses strategies from artificial intelligence and other computational techniques to solve complex optimization problems across various fields, such as engineering, finance, and operations research.
The interior-point method is an algorithmic approach used to solve linear programming problems, as well as certain types of nonlinear programming problems. It was introduced by Karmarkar in the 1980s and has become a popular alternative to the simplex method for large-scale optimization problems.
The "Killer heuristic" is a term often used in the context of artificial intelligence, particularly in search algorithms and optimization problems. It refers to a specific type of heuristic that significantly enhances the performance of search algorithms by allowing them to focus more effectively on promising regions of the search space. The name "Killer heuristic" comes from the idea that the heuristic "kills off" many of the less promising possibilities, thereby directing the search towards more fruitful areas.
The learning rate is a hyperparameter used in optimization algorithms, particularly in the context of machine learning and neural networks. It controls how much to change the model weights in response to the error or loss calculated during training. In more specific terms, the learning rate determines the size of the steps taken towards a minimum of the loss function during the training process.
The level-set method is a numerical technique used for tracking phase boundaries and interfaces in various fields, such as fluid dynamics, image processing, and computer vision. It was developed by Stanley Osher and James A. Sethian in 1988. ### Key Concepts: 1. **Level Set Function**: At its core, the level-set method represents a shape or interface implicitly as the zero contour of a higher-dimensional scalar function, known as the level-set function.
Lexicographic max-min optimization is a method used in multi-objective optimization problems where multiple criteria are involved. The approach prioritizes the objectives in a lexicographic order, meaning that the most important objective is optimized first. If there are multiple solutions for the first objective, the second most important objective is then optimized among those solutions, and this process continues down the list of objectives.