Vector optimization is a subarea of mathematical optimization where optimization problems with a vector-valued objective functions are optimized with respect...

Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute...

In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms...

single-objective optimization cases are presented. In the second part, test functions with their respective Pareto fronts for multi-objective optimization problems...

In certain optimization problems the unknown optimal solution might not be a number or a vector, but rather a continuous quantity, for example a function...

Andreas (2011). "Solution concepts in vector optimization: a fresh look at an old story" (PDF). Optimization. 60 (12): 1421–1440. doi:10.1080/02331931003665108...

minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support-vector machines...

generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from...

In mathematical optimization theory, duality or the duality principle is the principle that optimization problems may be viewed from either of two perspectives...

hyperparameter optimization methods. Bayesian optimization is a global optimization method for noisy black-box functions. Applied to hyperparameter optimization, Bayesian...

by using another overlaying optimizer, a concept known as meta-optimization, or even fine-tuned during the optimization, e.g., by means of fuzzy logic...

already been introduced, and was added to SGD optimization techniques in 1986. However, these optimization techniques assumed constant hyperparameters,...

A vector database, vector store or vector search engine is a database that uses the vector space model to store vectors (fixed-length lists of numbers)...

Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets (or, equivalently...

science and economics, an optimization problem is the problem of finding the best solution from all feasible solutions. Optimization problems can be divided...

representations of the computation being optimized and the optimization(s) being performed. Loop optimization can be viewed as the application of a sequence...

Bilevel optimization is a special kind of optimization where one problem is embedded (nested) within another. The outer optimization task is commonly referred...

Lexicographic optimization is a kind of Multi-objective optimization. In general, multi-objective optimization deals with optimization problems with two...

elements Automatic vectorization, a compiler optimization that transforms loops to vector operations Image tracing, the creation of vector from raster graphics...

explore properly efficient solutions of nonlinear vector optimization problems. In global optimization, he focused a good portion of his work on the theory...

optimization is a feature of many relational database management systems and other databases such as NoSQL and graph databases. The query optimizer attempts...

chemistry, energy minimization (also called energy optimization, geometry minimization, or geometry optimization) is the process of finding an arrangement in...

that can be vectorized, relying on the data dependence of the instructions inside loops. Automatic vectorization, like any loop optimization or other compile-time...

Topological optimization techniques can then help work around the limitations of pure shape optimization. Mathematically, shape optimization can be posed...

Multi-disciplinary design optimization (MDO) is a field of engineering that uses optimization methods to solve design problems incorporating a number...

predictive analytics. The key motivation behind multi-task optimization is that if optimization tasks are related to each other in terms of their optimal...

minimal optimization (SMO)-based algorithms employed by SVMs, which are guaranteed to find a global optimum (of the convex problem). The relevance vector machine...

Random optimization (RO) is a family of numerical optimization methods that do not require the gradient of the optimization problem and RO can hence be...

Gradient descent is a method for unconstrained mathematical optimization. It is a first-order iterative algorithm for minimizing a differentiable multivariate...

Vector space model or term vector model is an algebraic model for representing text documents (or more generally, items) as vectors such that the distance...