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

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

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

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

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

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

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

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

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...

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...

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

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

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

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

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

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

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

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...

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

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

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...

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

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...

Limited-memory BFGS (L-BFGS or LM-BFGS) is an optimization algorithm in the family of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–Shanno...

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