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Greedy triangulation is an algorithmic approach used in computational geometry to divide a polygon into triangles, which is a common step in various applications such as computer graphics, geographical information systems (GIS), and finite element analysis. The basic idea is to iteratively create a triangulation by making local, "greedy" choices. Here's a brief overview of how greedy triangulation works: 1. **Starting with a Polygon**: You begin with a simple polygon (which does not intersect itself).

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.

A hyper-heuristic is a high-level algorithm designed to select or generate heuristic algorithms to solve combinatorial optimization problems. Unlike traditional heuristics, which are problem-specific techniques that provide quick and approximate solutions, hyper-heuristics operate at a higher level of abstraction. Here are some key points about hyper-heuristics: 1. **Meta-Level Search**: Hyper-heuristics search through a space of heuristics (or heuristic components) rather than the solution space of the problem itself.

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.

IPOPT, short for Interior Point OPTimizer, is an open-source software package designed for solving large-scale nonlinear optimization problems. It is part of the COIN-OR (Computational Infrastructure for Operations Research) project and is particularly well-regarded for its efficient implementation of the interior-point method, which is a popular algorithm for nonlinear optimization.

The In-Crowd algorithm, also referred to as the In-Crowd filter or In-Crowd voting, is a method often used in the context of social networks, recommendation systems, and collaborative filtering. Its main objective is to leverage the preferences or behaviors of a well-defined community or group (the "in-crowd") to make predictions or recommendations tailored to users who belong to or are influenced by that group.

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.

Iterated Local Search (ILS) is a metaheuristic optimization algorithm used for solving combinatorial and continuous optimization problems. It is particularly effective for NP-hard problems. The method combines local search with a mechanism to escape local optima through perturbation, followed by a re-optimization of the solution. ### Key Components of Iterated Local Search: 1. **Initial Solution**: The algorithm starts with an initial feasible solution, which can be generated randomly or through some heuristics.

Karmarkar's algorithm is a polynomial-time algorithm for solving linear programming (LP) problems, developed by mathematician Narendra Karmarkar in 1984. The significance of the algorithm lies in its efficiency and its departure from the traditional simplex method, which, despite being widely used, can potentially take exponential time in the worst-case scenarios.

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.

Lemke's algorithm is a mathematical method used to find a solution to a class of problems known as linear complementarity problems (LCPs). An LCP involves finding a vector \( z \) such that: 1. \( Mz + q \geq 0 \) 2. \( z \geq 0 \) 3.

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.

The Levenberg–Marquardt algorithm is a popular optimization technique used for minimizing the sum of squared differences between observed data and a model. It is particularly effective for nonlinear least squares problems, where the aim is to fit a model to a set of data points. ### Key Features: 1. **Combination of Techniques**: The algorithm combines the gradient descent and the Gauss-Newton methods.

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.

Lexicographic optimization is a method used in multi-objective optimization problems where multiple objectives need to be optimized simultaneously. The approach prioritizes the objectives based on their importance or preference order. Here’s how it generally works: 1. **Ordering Objectives**: The first step in lexicographic optimization involves arranging the objectives in a hierarchy based on their priority. The most important objective is placed first, followed by the second most important, and so on.

Limited-memory BFGS (L-BFGS) is an optimization algorithm that is particularly efficient for solving large-scale unconstrained optimization problems. It is a quasi-Newton method, which means it uses approximations to the Hessian matrix (the matrix of second derivatives) to guide the search for a minimum.