SettingsMathematical optimization
2-opt
In optimization, 2-opt is a simple local search algorithm first proposed by Croes in 1958 for solving the traveling salesman problem.
In optimization, 2-opt is a simple local search algorithm first proposed by Croes in 1958 for solving the traveling salesman problem.
3-opt
In optimization, 3-opt is a simple local search algorithm for solving the traveling salesman problem and related network optimization problems.
In optimization, 3-opt is a simple local search algorithm for solving the traveling salesman problem and related network optimization problems.
Adaptive simulated annealing
Adaptive simulated annealing (ASA) is a variant of simulated annealing (SA) algorithm in which the algorithm parameters that control temperature schedule and random step selection are auto...
Adaptive simulated annealing (ASA) is a variant of simulated annealing (SA) algorithm in which the algorithm parameters that control temperature schedule and random step selection are auto...
AIMMS
AIMMS is a software system designed for modeling and solving large-scale optimization and scheduling-type problems.
AIMMS is a software system designed for modeling and solving large-scale optimization and scheduling-type problems.
Algebraic modeling language
Algebraic Modeling Languages are high-level computer programming languages for describing and solving high complexity problems for large scale mathematical computation.
Algebraic Modeling Languages are high-level computer programming languages for describing and solving high complexity problems for large scale mathematical computation.
AMPL
AMPL, an acronym for "A Mathematical Programming Language", is an algebraic modeling language for describing and solving high-complexity problems for large-scale mathematical computation (i.e.
AMPL, an acronym for "A Mathematical Programming Language", is an algebraic modeling language for describing and solving high-complexity problems for large-scale mathematical computation (i.e.
Analytica (software)
Analytica is a visual software package developed by Lumina Decision Systems for creating, analyzing and communicating quantitative decision models.
Analytica is a visual software package developed by Lumina Decision Systems for creating, analyzing and communicating quantitative decision models.
APMonitor
APMonitor, or "Advanced Process Monitor", is a modeling language for differential and algebraic equations.
APMonitor, or "Advanced Process Monitor", is a modeling language for differential and algebraic equations.
Backtracking line search
In (unconstrained) optimization, the backtracking linesearch strategy is used as part of a linesearch method, to compute how far one should move along a given search direction.
In (unconstrained) optimization, the backtracking linesearch strategy is used as part of a linesearch method, to compute how far one should move along a given search direction.
Backward induction
Backward induction is the process of reasoning backwards in time, from the end of a problem or situation, to determine a sequence of optimal actions.
Backward induction is the process of reasoning backwards in time, from the end of a problem or situation, to determine a sequence of optimal actions.
Barrier function
In constrained optimization, a field of mathematics, a barrier function is a continuous function whose value on a point increases to infinity as the point approaches the boundary of the feasibl...
In constrained optimization, a field of mathematics, a barrier function is a continuous function whose value on a point increases to infinity as the point approaches the boundary of the feasibl...
Bayesian efficiency
Bayesian efficiency addresses an appropriate economic definition of Pareto efficiency where there is incomplete information.
Bayesian efficiency addresses an appropriate economic definition of Pareto efficiency where there is incomplete information.
Bellman equation
A Bellman equation (also known as a dynamic programming equation), named after its discoverer, Richard Bellman, is a necessary condition for optimality associated with the mathematical opt...
A Bellman equation (also known as a dynamic programming equation), named after its discoverer, Richard Bellman, is a necessary condition for optimality associated with the mathematical opt...
Benders' decomposition
Benders' decomposition (alternatively, Benders's decomposition; named after Jacques F. Benders) is a technique in mathematical programming that allows the solution of very large linear pro...
Benders' decomposition (alternatively, Benders's decomposition; named after Jacques F. Benders) is a technique in mathematical programming that allows the solution of very large linear pro...
Bilevel program
In mathematics, bilevel programs are optimization problems where one optimization problem is embedded in another one.
In mathematics, bilevel programs are optimization problems where one optimization problem is embedded in another one.
Bilinear program
In mathematics, a bilinear program is a nonlinear optimization problem whose objective and/or constraint functions are bilinear.
In mathematics, a bilinear program is a nonlinear optimization problem whose objective and/or constraint functions are bilinear.
Binary constraint
Binary constraint, in mathematical optimization, is a constraint that involves exactly two variables.
Binary constraint, in mathematical optimization, is a constraint that involves exactly two variables.
Bregman method
Bregman's method is an iterative algorithm to solve certain convex optimization problems.
Bregman's method is an iterative algorithm to solve certain convex optimization problems.
Cake number
In mathematics, the cake number, denoted by Cn, is the maximum number of regions into which a 3-dimensional cube can be partitioned by exactly n planes.
In mathematics, the cake number, denoted by Cn, is the maximum number of regions into which a 3-dimensional cube can be partitioned by exactly n planes.
Calculus of variations
Calculus of variations is a field of mathematics, or more specifically calculus, that deals with maximizing or minimizing functionals, which are mappings from a set of functions to the real numbers.
Calculus of variations is a field of mathematics, or more specifically calculus, that deals with maximizing or minimizing functionals, which are mappings from a set of functions to the real numbers.
Candidate solution
In optimization, a candidate solution is a member of a set of possible solutions to a given problem.
In optimization, a candidate solution is a member of a set of possible solutions to a given problem.
Central composite design
In statistics, a central composite design is an experimental design, useful in response surface methodology, for building a second order (quadratic) model for the response variable without needi...
In statistics, a central composite design is an experimental design, useful in response surface methodology, for building a second order (quadratic) model for the response variable without needi...
Combinatorial data analysis
Combinatorial data analysis (CDA) is the study of data sets where the arrangement of objects is important.
Combinatorial data analysis (CDA) is the study of data sets where the arrangement of objects is important.
Complementarity theory
A complementarity problem is a type of mathematical optimization problem.
A complementarity problem is a type of mathematical optimization problem.
Compressed sensing
Compressed sensing, also known as compressive sensing, compressive sampling and sparse sampling, is a technique for finding sparse solutions to underdetermined linear systems.
Compressed sensing, also known as compressive sensing, compressive sampling and sparse sampling, is a technique for finding sparse solutions to underdetermined linear systems.
Conic optimization
Conic optimization is a subfield of convex optimization.
Conic optimization is a subfield of convex optimization.
Constraint (mathematics)
In mathematics, a constraint is a condition that a solution to an optimization problem must satisfy.
In mathematics, a constraint is a condition that a solution to an optimization problem must satisfy.
Constraint optimization
In constraint satisfaction, constraint optimization seeks for a solution maximizing or minimizing a cost function.
In constraint satisfaction, constraint optimization seeks for a solution maximizing or minimizing a cost function.
Continuous optimization
Continuous optimization is a branch of optimization in applied mathematics.
Continuous optimization is a branch of optimization in applied mathematics.
Convex analysis
Convex analysis is the branch of mathematics devoted to the study of properties of convex functions and convex sets, often with applications in convex minimization, a subdomain of optimization t...
Convex analysis is the branch of mathematics devoted to the study of properties of convex functions and convex sets, often with applications in convex minimization, a subdomain of optimization t...
Convex optimization
Convex minimization, a subfield of optimization, studies the problem of minimizing convex functions over convex sets.
Convex minimization, a subfield of optimization, studies the problem of minimizing convex functions over convex sets.
Corner solution
A corner solution is a special solution to an agent's maximization problem in which the quantity of one of the arguments in the maximized function is zero.
A corner solution is a special solution to an agent's maximization problem in which the quantity of one of the arguments in the maximized function is zero.
Dantzig-Wolfe decomposition
Dantzig–Wolfe decomposition is an algorithm for solving linear programming problems with special structure.
Dantzig–Wolfe decomposition is an algorithm for solving linear programming problems with special structure.
Dantzig–Wolfe decomposition
Dantzig–Wolfe decomposition is an algorithm for solving linear programming problems with special structure.
Dantzig–Wolfe decomposition is an algorithm for solving linear programming problems with special structure.
Database tuning
Database tuning describes a group of activities used to optimize and homogenize the performance of a database.
Database tuning describes a group of activities used to optimize and homogenize the performance of a database.
Dead-end elimination
The dead-end elimination algorithm (DEE) is a method for minimizing a function over a discrete set of independent variables.
The dead-end elimination algorithm (DEE) is a method for minimizing a function over a discrete set of independent variables.
Demand optimization
Demand optimization is the application of processes and tools to maximize return on sales.
Demand optimization is the application of processes and tools to maximize return on sales.
Differential evolution
In computer science, differential evolution (DE) is a method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.
In computer science, differential evolution (DE) is a method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.
Discrete optimization
Discrete optimization is a branch of optimization in applied mathematics and computer science.
Discrete optimization is a branch of optimization in applied mathematics and computer science.
Distributed constraint optimization
Distributed constraint optimization (DCOP or DisCOP) is the distributed analogue to constraint optimization.
Distributed constraint optimization (DCOP or DisCOP) is the distributed analogue to constraint optimization.
DNSS point
DNSS points arise in optimal control problems that exhibit multiple optimal solutions.
DNSS points arise in optimal control problems that exhibit multiple optimal solutions.
Dual cone and polar cone
Dual cone and polar cone are closely related concepts in convex analysis, a branch of mathematics.
Dual cone and polar cone are closely related concepts in convex analysis, a branch of mathematics.
Dual problem
In constrained optimization, it is often possible to convert the primal problem to a dual form, which is termed a dual problem.
In constrained optimization, it is often possible to convert the primal problem to a dual form, which is termed a dual problem.
Duality (optimization)
The duality gap is used in certain optimization methods to determine how far off from optimality the current solution is.
The duality gap is used in certain optimization methods to determine how far off from optimality the current solution is.
Duality gap
In optimization problems in applied mathematics, the duality gap is the difference between the primal and dual solutions.
In optimization problems in applied mathematics, the duality gap is the difference between the primal and dual solutions.
Dynamic programming
In mathematics and computer science, dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems.
In mathematics and computer science, dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems.
Energy minimization
In computational chemistry, energy minimization (also called energy optimization or geometry optimization) methods are used to compute the equilibrium configuration of molecules and ...
In computational chemistry, energy minimization (also called energy optimization or geometry optimization) methods are used to compute the equilibrium configuration of molecules and ...
Extended newsvendor model
Extended newsvendor models are variations of the classic newsvendor model involving production capacity constraints, multiple products, multiple production cycles, demand dependent selling price...
Extended newsvendor models are variations of the classic newsvendor model involving production capacity constraints, multiple products, multiple production cycles, demand dependent selling price...
Extended Newsvendor models
Extended Newsvendor models are some variations of the classic Newsvendor problem involving production capacity constraints, multiple products, multiple production cycles, demand dependent sellin...
Extended Newsvendor models are some variations of the classic Newsvendor problem involving production capacity constraints, multiple products, multiple production cycles, demand dependent sellin...
Farkas' lemma
Farkas' lemma is a result in mathematics stating that a vector is either in a given convex cone or that there exists a (hyper)plane separating the vector from the cone, but not both.
Farkas' lemma is a result in mathematics stating that a vector is either in a given convex cone or that there exists a (hyper)plane separating the vector from the cone, but not both.
Fenchel's duality theorem
In mathematics, Fenchel's duality theorem is a result in the theory of convex functions named after Werner Fenchel.
In mathematics, Fenchel's duality theorem is a result in the theory of convex functions named after Werner Fenchel.
Fritz John conditions
In mathematics, the Fritz John conditions (abbr.
In mathematics, the Fritz John conditions (abbr.
Genetic algorithm
In the computer science field of artificial intelligence, a genetic algorithm is a search heuristic that mimics the process of natural evolution.
In the computer science field of artificial intelligence, a genetic algorithm is a search heuristic that mimics the process of natural evolution.
Genetic programming
In artificial intelligence, genetic programming is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task.
In artificial intelligence, genetic programming is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task.
Geometric median
The geometric median of a discrete set of sample points in a Euclidean space is the point minimizing the sum of distances to the sample points.
The geometric median of a discrete set of sample points in a Euclidean space is the point minimizing the sum of distances to the sample points.
Global optimization
Global optimization is a branch of applied mathematics and numerical analysis that deals with the optimization of a function or a set of functions to some criteria.
Global optimization is a branch of applied mathematics and numerical analysis that deals with the optimization of a function or a set of functions to some criteria.
Global optimum
In mathematics, a global optimum is a selection from a given domain which yields either the highest value or lowest value (depending on the objective), when a specific function is applied.
In mathematics, a global optimum is a selection from a given domain which yields either the highest value or lowest value (depending on the objective), when a specific function is applied.
Goal programming
Goal programming is a branch of multiobjective optimization, which in turn is a branch of multi-criteria decision analysis (MCDA), also known as multiple-criteria decision making (MCDM).
Goal programming is a branch of multiobjective optimization, which in turn is a branch of multi-criteria decision analysis (MCDA), also known as multiple-criteria decision making (MCDM).
Google matrix
A Google matrix is a particular stochastic matrix that is used by Google's PageRank algorithm.
A Google matrix is a particular stochastic matrix that is used by Google's PageRank algorithm.
Guess value
A Guess value is more commonly called a starting value or initial value.
A Guess value is more commonly called a starting value or initial value.
Hardness of approximation
In computer science, hardness of approximation is a field that studies the complexity of finding near-optimal solutions to optimization problems.
In computer science, hardness of approximation is a field that studies the complexity of finding near-optimal solutions to optimization problems.
Highly optimized tolerance
In applied mathematics, highly optimized tolerance (HOT) is a method of generating power law behavior in systems by including a global optimization principle.
In applied mathematics, highly optimized tolerance (HOT) is a method of generating power law behavior in systems by including a global optimization principle.
Hilbert basis (linear programming)
In linear programming, a Hilbert basis for a convex cone is an integer cone basis: minimal set of integer vectors such that every integer vector in the convex cone is a linear combination of the...
In linear programming, a Hilbert basis for a convex cone is an integer cone basis: minimal set of integer vectors such that every integer vector in the convex cone is a linear combination of the...
Himmelblau's function
In mathematical optimization, the Himmelblau's function is a multi-modal function, used to test the performance of optimization algorithms.
In mathematical optimization, the Himmelblau's function is a multi-modal function, used to test the performance of optimization algorithms.
Homicidal chauffeur problem
In game theory, the homicidal chauffeur problem is a mathematical pursuit problem which pits a hypothetical runner, who can only move slowly, but is highly maneuverable, against the driver of a ...
In game theory, the homicidal chauffeur problem is a mathematical pursuit problem which pits a hypothetical runner, who can only move slowly, but is highly maneuverable, against the driver of a ...
Hyper-heuristic
A hyper-heuristic is a heuristic search method that seeks to automate, often by the incorporation of machine learning techniques, the process of selecting, combining, generating or adapting seve...
A hyper-heuristic is a heuristic search method that seeks to automate, often by the incorporation of machine learning techniques, the process of selecting, combining, generating or adapting seve...
Infinite-dimensional optimization
Such a problem is an infinite-dimensional optimization problem, because, a continuous quantity cannot be determined by a finite number of certain degrees of freedom.
Such a problem is an infinite-dimensional optimization problem, because, a continuous quantity cannot be determined by a finite number of certain degrees of freedom.
Inventory control problem
The inventory control problem is the problem faced by a firm that must decide how much to order in each time period to meet demand for its products.
The inventory control problem is the problem faced by a firm that must decide how much to order in each time period to meet demand for its products.
Iterated conditional modes
In statistics, iterated conditional modes is a deterministic algorithm for obtaining the configuration that maximizes the joint probability of a Markov random field.
In statistics, iterated conditional modes is a deterministic algorithm for obtaining the configuration that maximizes the joint probability of a Markov random field.
Jeep problem
The jeep problem, desert crossing problem or exploration problem is a mathematics problem in which a jeep must maximise the distance it can travel into a desert with a given quantity...
The jeep problem, desert crossing problem or exploration problem is a mathematics problem in which a jeep must maximise the distance it can travel into a desert with a given quantity...
Job shop scheduling
Job shop scheduling is an optimization problem in computer science in which ideal jobs are assigned to resources at particular times.
Job shop scheduling is an optimization problem in computer science in which ideal jobs are assigned to resources at particular times.
Job-shop problem
The job-shop problem (JSP) is a problem in discrete or combinatorial optimization, and is a generalization of the famous travelling salesman problem.
The job-shop problem (JSP) is a problem in discrete or combinatorial optimization, and is a generalization of the famous travelling salesman problem.
Karush-Kuhn-Tucker conditions
In mathematics, the Karush–Kuhn–Tucker conditions (also known as the Kuhn–Tucker or KKT conditions) are necessary for a solution in nonlinear programming to be optimal, provided that...
In mathematics, the Karush–Kuhn–Tucker conditions (also known as the Kuhn–Tucker or KKT conditions) are necessary for a solution in nonlinear programming to be optimal, provided that...
Karush-Kuhn–Tucker conditions
In mathematics, the Karush–Kuhn–Tucker conditions are necessary for a solution in nonlinear programming to be optimal, provided that some regularity conditions are satisfied.
In mathematics, the Karush–Kuhn–Tucker conditions are necessary for a solution in nonlinear programming to be optimal, provided that some regularity conditions are satisfied.
Karush–Kuhn–Tucker conditions
In mathematics, the Karush–Kuhn–Tucker conditions (also known as the Kuhn–Tucker or KKT conditions) are necessary for a solution in nonlinear programming to be optimal, provided that...
In mathematics, the Karush–Kuhn–Tucker conditions (also known as the Kuhn–Tucker or KKT conditions) are necessary for a solution in nonlinear programming to be optimal, provided that...
Klee–Minty cube
The Klee–Minty cube has been used to analyze the behavior of many algorithms, both in the worst case and on average.
The Klee–Minty cube has been used to analyze the behavior of many algorithms, both in the worst case and on average.
Lagrange multiplier
In mathematical optimization, the method of Lagrange multipliers provides a strategy for finding the local maxima and minima of a function subject to equality constraints.
In mathematical optimization, the method of Lagrange multipliers provides a strategy for finding the local maxima and minima of a function subject to equality constraints.
Lagrange multipliers on Banach spaces
In the field of calculus of variations in mathematics, the method of Lagrange multipliers on Banach spaces can be used to solve certain infinite-dimensional constrained optimization problems.
In the field of calculus of variations in mathematics, the method of Lagrange multipliers on Banach spaces can be used to solve certain infinite-dimensional constrained optimization problems.
Lagrangian relaxation
In the field of mathematical optimization, Lagrangian relaxation is a relaxation method which approximates a difficult problem of constrained optimization by an simpler problem.
In the field of mathematical optimization, Lagrangian relaxation is a relaxation method which approximates a difficult problem of constrained optimization by an simpler problem.
Lazy caterer's sequence
The lazy caterer's sequence, more formally known as the central polygonal numbers, describes the maximum number of pieces of a circle (a pancake or pizza is usually used to describe the si...
The lazy caterer's sequence, more formally known as the central polygonal numbers, describes the maximum number of pieces of a circle (a pancake or pizza is usually used to describe the si...
Least absolute deviations
Least absolute deviations, also known as Least Absolute Errors, Least Absolute Value, or the L1 norm problem, is a mathematical optimization technique similar to the popular least square...
Least absolute deviations, also known as Least Absolute Errors, Least Absolute Value, or the L1 norm problem, is a mathematical optimization technique similar to the popular least square...
Least squares
The method of least squares is a standard approach to the approximate solution of overdetermined systems, i.e., sets of equations in which there are more equations than unknowns.
The method of least squares is a standard approach to the approximate solution of overdetermined systems, i.e., sets of equations in which there are more equations than unknowns.
Lemke's algorithm
In mathematical optimization, Lemke's algorithm is an procedure for solving linear complementarity problems, and more generally mixed linear complementarity problems.
In mathematical optimization, Lemke's algorithm is an procedure for solving linear complementarity problems, and more generally mixed linear complementarity problems.
Level set method
The level set method (sometimes abbreviated as LSM) is a numerical technique for tracking interfaces and shapes.
The level set method (sometimes abbreviated as LSM) is a numerical technique for tracking interfaces and shapes.
Linear complementarity problem
In mathematical optimization theory, the linear complementarity problem (LCP) arises frequently in computational mechanics and encompasses the well-known quadratic programming as a special case.
In mathematical optimization theory, the linear complementarity problem (LCP) arises frequently in computational mechanics and encompasses the well-known quadratic programming as a special case.
Linear programming decoding
In information theory and coding theory, linear programming decoding (LP decoding) is a decoding method which uses concepts from LP theory to solve decoding problems.
In information theory and coding theory, linear programming decoding (LP decoding) is a decoding method which uses concepts from LP theory to solve decoding problems.
Linear search problem
In computational complexity theory, the Linear search problem is an optimal search problem introduced by Richard E. Bellman.
In computational complexity theory, the Linear search problem is an optimal search problem introduced by Richard E. Bellman.
Linear-fractional programming
In mathematical optimization, linear-fractional programming (LFP) is a generalization of linear programming (LP).
In mathematical optimization, linear-fractional programming (LFP) is a generalization of linear programming (LP).
LIONsolver
LIONsolver is an integrated software for data mining, business intelligence, and modeling implementing the Learning and Intelligent OptimizatioN and reactive business intelligence approach.
LIONsolver is an integrated software for data mining, business intelligence, and modeling implementing the Learning and Intelligent OptimizatioN and reactive business intelligence approach.
Lloyd's algorithm
In computer science and electrical engineering, Lloyd's algorithm, also known as Voronoi iteration or relaxation, is an algorithm for grouping data points into a given number of categories...
In computer science and electrical engineering, Lloyd's algorithm, also known as Voronoi iteration or relaxation, is an algorithm for grouping data points into a given number of categories...
Local optimum
Local optimum is a term in applied mathematics and computer science.
Local optimum is a term in applied mathematics and computer science.
Low rank approximation
Low rank approximation is a minimization problem, in which the cost function measures the fit between a given matrix and an approximating matrix, subject to a constraint that the approximating m...
Low rank approximation is a minimization problem, in which the cost function measures the fit between a given matrix and an approximating matrix, subject to a constraint that the approximating m...
Low-rank approximation
In mathematics, low-rank approximation is a minimization problem, in which the cost function measures the fit between a given matrix and an approximating matrix, subject to a constraint that the...
In mathematics, low-rank approximation is a minimization problem, in which the cost function measures the fit between a given matrix and an approximating matrix, subject to a constraint that the...
Marketing science
Marketing science is a field that approaches marketing -- the understanding of customer needs, and the development of approaches by which they might be fulfilled -- predominantly through the met...
Marketing science is a field that approaches marketing -- the understanding of customer needs, and the development of approaches by which they might be fulfilled -- predominantly through the met...
Mathematical optimization
In mathematics, computer science and economics, optimization, or mathematical programming, refers to choosing the best element from some set of available alternatives.
In mathematics, computer science and economics, optimization, or mathematical programming, refers to choosing the best element from some set of available alternatives.
Mathematical Programming Society
Known as the Mathematical Programming Society until 2010, the Mathematical Optimization Society (MOS) is an international association of researchers active in optimization.
Known as the Mathematical Programming Society until 2010, the Mathematical Optimization Society (MOS) is an international association of researchers active in optimization.
Mathematical programming with equilibrium constraints
Mathematical programming with equilibrium constraints (MPEC) is the study of constrained optimization problems where the constraints include variational inequalities or complementarities.
Mathematical programming with equilibrium constraints (MPEC) is the study of constrained optimization problems where the constraints include variational inequalities or complementarities.
Mathematics of Operations Research
Mathematics of Operations Research (MOR) is a scholarly journal published since 1976.
Mathematics of Operations Research (MOR) is a scholarly journal published since 1976.
Matheuristics
Matheuristics are optimization algorithms made by the interoperation of metaheuristics and mathematical programming techniques.
Matheuristics are optimization algorithms made by the interoperation of metaheuristics and mathematical programming techniques.
Maxima and minima
In mathematics, the maximum and minimum (plural: maxima and minima) of a function, known collectively as extrema (singular: extremum), are the largest and smallest value that the fun...
In mathematics, the maximum and minimum (plural: maxima and minima) of a function, known collectively as extrema (singular: extremum), are the largest and smallest value that the fun...
Maximum theorem
The maximum theorem provides conditions for the continuity of an optimized function and the set of its maximizers as a parameter changes.
The maximum theorem provides conditions for the continuity of an optimized function and the set of its maximizers as a parameter changes.
MCACEA
MCACEA is a general framework that uses a single evolutionary algorithm per agent sharing their optimal solutions to coordinate the evolutions of the EAs populations using cooperation objectives.
MCACEA is a general framework that uses a single evolutionary algorithm per agent sharing their optimal solutions to coordinate the evolutions of the EAs populations using cooperation objectives.
Meta-optimization
In numerical optimization, meta-optimization is the use of one optimization method to tune another optimization method.
In numerical optimization, meta-optimization is the use of one optimization method to tune another optimization method.
Metaheuristic
In computer science, metaheuristic designates a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.
In computer science, metaheuristic designates a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.
Mixed complementarity problem
Mixed Complementarity Problem (MCP) is a problem formulation in mathematical programming.
Mixed Complementarity Problem (MCP) is a problem formulation in mathematical programming.
Mixed linear complementarity problem
In mathematical optimization theory, the mixed linear complementarity problem, often abbreviated as MLCP or LMCP, is a generalization of the linear complementarity problem to include...
In mathematical optimization theory, the mixed linear complementarity problem, often abbreviated as MLCP or LMCP, is a generalization of the linear complementarity problem to include...
Moving least squares
Moving least squares is a method of reconstructing continuous functions from a set of unorganized point samples via the calculation of a weighted least squares measure biased towards the region ...
Moving least squares is a method of reconstructing continuous functions from a set of unorganized point samples via the calculation of a weighted least squares measure biased towards the region ...
MPS (format)
MPS (Mathematical Programming System) is a file format for presenting and archiving linear programming (LP) and mixed integer programming problems.
MPS (Mathematical Programming System) is a file format for presenting and archiving linear programming (LP) and mixed integer programming problems.
Multi-objective optimization
Multi-objective optimization, also known as multi-criteria or multi-attribute optimization, is the process of simultaneously optimizing two or more conflicting objectives subject to ...
Multi-objective optimization, also known as multi-criteria or multi-attribute optimization, is the process of simultaneously optimizing two or more conflicting objectives subject to ...
Multidisciplinary design optimization
Multi-disciplinary design optimization is a field of engineering that uses optimization methods to solve design problems incorporating a number of disciplines.
Multi-disciplinary design optimization is a field of engineering that uses optimization methods to solve design problems incorporating a number of disciplines.
Multiprocessor scheduling
In computer science, multiprocessor scheduling is an NP-complete optimization problem.
In computer science, multiprocessor scheduling is an NP-complete optimization problem.
Nearest neighbor search
Nearest neighbor search (NNS), also known as proximity search, similarity search or closest point search, is an optimization problem for finding closest points in metric ...
Nearest neighbor search (NNS), also known as proximity search, similarity search or closest point search, is an optimization problem for finding closest points in metric ...
Newsvendor
The newsvendor model is a mathematical model in operations management and applied economics used to determine optimal inventory levels.
The newsvendor model is a mathematical model in operations management and applied economics used to determine optimal inventory levels.
Newsvendor model
The newsvendor (or newsboy or single-period) model is a mathematical model in operations management and applied economics used to determine optimal inventory levels.
The newsvendor (or newsboy or single-period) model is a mathematical model in operations management and applied economics used to determine optimal inventory levels.
nl (format)
nl is a file format for presenting and archiving mathematical programming problems.
nl is a file format for presenting and archiving mathematical programming problems.
Non-linear least squares
Non-linear least squares is the form of least squares analysis which is used to fit a set of m observations with a model that is non-linear in n unknown parameters.
Non-linear least squares is the form of least squares analysis which is used to fit a set of m observations with a model that is non-linear in n unknown parameters.
Nonlinear programming
In mathematics, nonlinear programming (NLP) is the process of solving a system of equalities and inequalities, collectively termed constraints, over a set of unknown real variables, along ...
In mathematics, nonlinear programming (NLP) is the process of solving a system of equalities and inequalities, collectively termed constraints, over a set of unknown real variables, along ...
NP-complete
In computational complexity theory, the complexity class NP-complete is a class of decision problems.
In computational complexity theory, the complexity class NP-complete is a class of decision problems.
Odds algorithm
The odds-algorithm is a mathematical method to compute optimal strategies for a class of problems that belong to the domain of optimal stopping.
The odds-algorithm is a mathematical method to compute optimal strategies for a class of problems that belong to the domain of optimal stopping.
Open Shop Scheduling
The Open Shop Scheduling Problem (OSSP) is a scheduling problem where, given n jobs and m workstations, each job has to visit a workstation at least once.
The Open Shop Scheduling Problem (OSSP) is a scheduling problem where, given n jobs and m workstations, each job has to visit a workstation at least once.
Open shop scheduling
The open shop scheduling problem (OSSP) is a scheduling problem where, given n jobs and m workstations, each job has to visit a workstation at least once.
The open shop scheduling problem (OSSP) is a scheduling problem where, given n jobs and m workstations, each job has to visit a workstation at least once.
Operations research
Operations research, or Operational Research in British usage, is a discipline that deals with the application of advanced analytical methods to help make better decisions.
Operations research, or Operational Research in British usage, is a discipline that deals with the application of advanced analytical methods to help make better decisions.
Optimal control
Optimal control theory, an extension of the calculus of variations, is a mathematical optimization method for deriving control policies.
Optimal control theory, an extension of the calculus of variations, is a mathematical optimization method for deriving control policies.
Optimal design
Optimal designs are a class of experimental designs that are optimal with respect to some statistical criterion.
Optimal designs are a class of experimental designs that are optimal with respect to some statistical criterion.
Optimal stopping
In mathematics, the theory of optimal stopping is concerned with the problem of choosing a time to take a particular action, in order to maximise an expected reward or minimise an expected cost.
In mathematics, the theory of optimal stopping is concerned with the problem of choosing a time to take a particular action, in order to maximise an expected reward or minimise an expected cost.
Optimal substructure
In computer science, a problem is said to have optimal substructure if an optimal solution can be constructed efficiently from optimal solutions to its subproblems.
In computer science, a problem is said to have optimal substructure if an optimal solution can be constructed efficiently from optimal solutions to its subproblems.
Optimization (mathematics)
In mathematics, computer science and economics, optimization, or mathematical programming, refers to choosing the best element from some set of available alternatives.
In mathematics, computer science and economics, optimization, or mathematical programming, refers to choosing the best element from some set of available alternatives.
Ordinal optimization
In mathematical optimization, ordinal optimization is the maximization of functions taking values in a partially ordered set.
In mathematical optimization, ordinal optimization is the maximization of functions taking values in a partially ordered set.
Oriented matroid
An oriented matroid is a mathematical structure that abstracts the properties of directed graphs and of arrangements of vectors in a vector space over an ordered field (particularly for partial...
An oriented matroid is a mathematical structure that abstracts the properties of directed graphs and of arrangements of vectors in a vector space over an ordered field (particularly for partial...
Paper bag problem
In geometry, the paper bag problem or teabag problem involves calculating the maximum possible inflated volume of an initially flat sealed rectangular bag which has the same shape as a cus...
In geometry, the paper bag problem or teabag problem involves calculating the maximum possible inflated volume of an initially flat sealed rectangular bag which has the same shape as a cus...
Paradiseo
ParadisEO is a white-box object-oriented framework dedicated to the flexible design of metaheuristics.
ParadisEO is a white-box object-oriented framework dedicated to the flexible design of metaheuristics.
Parallel metaheuristic
ParadisEO is a white-box object-oriented framework dedicated to the flexible design of metaheuristics.
ParadisEO is a white-box object-oriented framework dedicated to the flexible design of metaheuristics.
Pareto efficiency
Pareto efficiency, or Pareto optimality, is a concept in economics with applications in engineering and social sciences.
Pareto efficiency, or Pareto optimality, is a concept in economics with applications in engineering and social sciences.
Pattern search (optimization)
Pattern search is a family of numerical optimization methods that do not require the gradient of the problem to be optimized.
Pattern search is a family of numerical optimization methods that do not require the gradient of the problem to be optimized.
Perturbation function
In mathematical optimization, the perturbation function is any function which relates to primal and dual problems.
In mathematical optimization, the perturbation function is any function which relates to primal and dual problems.
Pontryagin's minimum principle
Pontryagin's minimum principle is used in optimal control theory to find the best possible control for taking a dynamical system from one state to another, especially in the presence of constrai...
Pontryagin's minimum principle is used in optimal control theory to find the best possible control for taking a dynamical system from one state to another, especially in the presence of constrai...
Process optimization
Process optimization is the discipline of adjusting a process so as to optimize some specified set of parameters without violating some constraint.
Process optimization is the discipline of adjusting a process so as to optimize some specified set of parameters without violating some constraint.
Pseudoconvex function
In convex analysis and the calculus of variations, branches of mathematics, a pseudoconvex function is a function that behaves like a convex function with respect to finding its local minima, bu...
In convex analysis and the calculus of variations, branches of mathematics, a pseudoconvex function is a function that behaves like a convex function with respect to finding its local minima, bu...
Quadratic programming
Quadratic programming (QP) is a special type of mathematical optimization problem.
Quadratic programming (QP) is a special type of mathematical optimization problem.
Quadratically constrained quadratic program
In mathematics, a quadratically constrained quadratic program is an optimization problem in which both the objective function and the constraints are quadratic functions.
In mathematics, a quadratically constrained quadratic program is an optimization problem in which both the objective function and the constraints are quadratic functions.
Random optimization
Random optimization (RO) is a family of numerical optimization methods that do not require the gradient of the problem to be optimized and RO can hence be used on functions that are not continuo...
Random optimization (RO) is a family of numerical optimization methods that do not require the gradient of the problem to be optimized and RO can hence be used on functions that are not continuo...
Rastrigin function
In mathematical optimization, the Rastrigin function is a non-convex function used as a performance test problem for optimization algorithms.
In mathematical optimization, the Rastrigin function is a non-convex function used as a performance test problem for optimization algorithms.
Reduced cost
In linear programming, reduced cost, or opportunity cost, is the amount by which an objective function coefficient would have to improve (so increase for maximization problem, decrease for...
In linear programming, reduced cost, or opportunity cost, is the amount by which an objective function coefficient would have to improve (so increase for maximization problem, decrease for...
Relaxation (approximation)
In mathematical optimization and related fields, relaxation is a modeling strategy.
In mathematical optimization and related fields, relaxation is a modeling strategy.
Relaxation technique (mathematics)
A relaxation technique is a method in mathematical optimization for relaxing a strict requirement, by either substituting for it another more easily handled requirement or else dropping it compl...
A relaxation technique is a method in mathematical optimization for relaxing a strict requirement, by either substituting for it another more easily handled requirement or else dropping it compl...
Response surface methodology
In statistics, response surface methodology explores the relationships between several explanatory variables and one or more response variables.
In statistics, response surface methodology explores the relationships between several explanatory variables and one or more response variables.
Robbins' problem
In probability theory, Robbins' problem of optimal stopping, named after Herbert Robbins, is sometimes referred to as the fourth secretary problem or the problem of minimizing the expected rank ...
In probability theory, Robbins' problem of optimal stopping, named after Herbert Robbins, is sometimes referred to as the fourth secretary problem or the problem of minimizing the expected rank ...
Robust optimization
Robust optimization is a field of optimization theory that deals with optimization problems where robustness is sought against uncertainty and/or variability in the value of a parameter of the p...
Robust optimization is a field of optimization theory that deals with optimization problems where robustness is sought against uncertainty and/or variability in the value of a parameter of the p...
Rosenbrock function
In mathematical optimization, the Rosenbrock function is a non-convex function used as a performance test problem for optimization algorithms introduced by Howard H. Rosenbrock.
In mathematical optimization, the Rosenbrock function is a non-convex function used as a performance test problem for optimization algorithms introduced by Howard H. Rosenbrock.
Semi-continuity
In mathematical analysis, semi-continuity (or semicontinuity) is a property of extended real-valued functions that is weaker than continuity.
In mathematical analysis, semi-continuity (or semicontinuity) is a property of extended real-valued functions that is weaker than continuity.
Semidefinite programming
Semidefinite programming is a subfield of convex optimization concerned with the optimization of a linear objective function over the intersection of the cone of positive semidefinite matrices ...
Semidefinite programming is a subfield of convex optimization concerned with the optimization of a linear objective function over the intersection of the cone of positive semidefinite matrices ...
Shape optimization
Shape optimization is part of the field of optimal control theory.
Shape optimization is part of the field of optimal control theory.
Shekel function
Shekel function is a multidimensional, multimodal, continuous, deterministic function commonly used as a test function for testing optimization techniques.
Shekel function is a multidimensional, multimodal, continuous, deterministic function commonly used as a test function for testing optimization techniques.
Signomial
A "signomial" is an algebraic function of one or more independent variables.
A "signomial" is an algebraic function of one or more independent variables.
Single machine scheduling
Single machine scheduling or single resource scheduling is the process of assigning a group of tasks to a single machine or resource.
Single machine scheduling or single resource scheduling is the process of assigning a group of tasks to a single machine or resource.
Single-machine scheduling
Single-machine scheduling or single-resource scheduling is the process of assigning a group of tasks to a single machine or resource.
Single-machine scheduling or single-resource scheduling is the process of assigning a group of tasks to a single machine or resource.
Sion's minimax theorem
In mathematics, and in particular game theory, Sion's minimax theorem is a generalization of John von Neumann's minimax theorem.
In mathematics, and in particular game theory, Sion's minimax theorem is a generalization of John von Neumann's minimax theorem.
Slack variable
In linear programming, a slack variable is a variable that is added to a constraint to turn the inequality into an equation.
In linear programming, a slack variable is a variable that is added to a constraint to turn the inequality into an equation.
Slater's condition
In mathematics, Slater's condition (or Slater condition) is a sufficient condition for strong duality to hold for a convex optimization problem.
In mathematics, Slater's condition (or Slater condition) is a sufficient condition for strong duality to hold for a convex optimization problem.
sol (format)
sol is a file format for representing solutions of mathematical programming problems.
sol is a file format for representing solutions of mathematical programming problems.
Special ordered set
In discrete optimization, a special ordered set is an ordered set of variables, used as an additional way to specify integrality conditions in an optimization model.
In discrete optimization, a special ordered set is an ordered set of variables, used as an additional way to specify integrality conditions in an optimization model.
Starmad
STARMAD (Space Tool for Advanced and Rapid Mission Analysis and Design) deals with the latest trend in the space industry is towards space missions, spa...
STARMAD (Space Tool for Advanced and Rapid Mission Analysis and Design) deals with the latest trend in the space industry is towards space missions, spa...
Stigler diet
The Stigler diet is an optimization problem named for George Stigler, a 1982 Nobel Laureate in economics, who posed the following problem: For a moderately active man weighing 154 pounds, how m...
The Stigler diet is an optimization problem named for George Stigler, a 1982 Nobel Laureate in economics, who posed the following problem: For a moderately active man weighing 154 pounds, how m...
Stochastic programming
Stochastic programming is a framework for modeling optimization problems that involve uncertainty.
Stochastic programming is a framework for modeling optimization problems that involve uncertainty.
Strong duality
Strong duality is a concept in optimization such that the primal and dual solutions are equivalent.
Strong duality is a concept in optimization such that the primal and dual solutions are equivalent.
Subderivative
In mathematics, the concepts of subderivative, subgradient, and subdifferential arise in convex analysis, that is, in the study of convex functions, often in connection to convex opt...
In mathematics, the concepts of subderivative, subgradient, and subdifferential arise in convex analysis, that is, in the study of convex functions, often in connection to convex opt...
Subgradient method
Subgradient methods are iterative methods for solving convex minimization problems.
Subgradient methods are iterative methods for solving convex minimization problems.
Successive linear programming
Successive Linear Programming (SLP), also known as Sequential Linear Programming, is an optimization technique for approximately solving nonlinear optimization problems.
Successive Linear Programming (SLP), also known as Sequential Linear Programming, is an optimization technique for approximately solving nonlinear optimization problems.
Sum-of-squares optimization
Sum-of-squares optimization techniques have been successfully applied by researchers in the control engineering field.
Sum-of-squares optimization techniques have been successfully applied by researchers in the control engineering field.
Ternary search
A ternary search algorithm is a technique in computer science for finding the minimum or maximum of a unimodal function (function that is either strictly increasing and then strictly decreasing ...
A ternary search algorithm is a technique in computer science for finding the minimum or maximum of a unimodal function (function that is either strictly increasing and then strictly decreasing ...
Topological derivative
In the field of shape optimization, a topological derivative is, conceptually, a derivative of a function of a region with respect to infinitesimal changes in its topology, such as adding an inf...
In the field of shape optimization, a topological derivative is, conceptually, a derivative of a function of a region with respect to infinitesimal changes in its topology, such as adding an inf...
Topology optimization
Topology optimization is distinct from shape optimization since typically shape optimisation methods work in a subset of allowable shapes which have fixed topological properties, such as having ...
Topology optimization is distinct from shape optimization since typically shape optimisation methods work in a subset of allowable shapes which have fixed topological properties, such as having ...
Trajectory optimization
Trajectory optimization is the process of designing a trajectory that minimizes or maximizes some measure of performance within prescribed constraint boundaries.
Trajectory optimization is the process of designing a trajectory that minimizes or maximizes some measure of performance within prescribed constraint boundaries.
Trust region
Trust region is a term used in mathematical optimization to denote the subset of the region of the objective function to be optimized that is approximated using a model function (often a quadratic).
Trust region is a term used in mathematical optimization to denote the subset of the region of the objective function to be optimized that is approximated using a model function (often a quadratic).
UniSoma
Located in Campinas, in São Paulo (state), UniSoma develops and deploys decision support solutions for industrial, agribusiness and logistics chains processes, also known as Supply Chain Plannin...
Located in Campinas, in São Paulo (state), UniSoma develops and deploys decision support solutions for industrial, agribusiness and logistics chains processes, also known as Supply Chain Plannin...
Variational Monte Carlo
In mathematical physics, variational Monte Carlo (VMC) is a quantum Monte Carlo method that applies the variational method to approximate the ground state of the system.
In mathematical physics, variational Monte Carlo (VMC) is a quantum Monte Carlo method that applies the variational method to approximate the ground state of the system.
Vector optimization
Vector optimization is an optimization problem of simultaneously optimizing multiple objective functions subject to constraints and a given ordering.
Vector optimization is an optimization problem of simultaneously optimizing multiple objective functions subject to constraints and a given ordering.
Wald's maximin model
In decision theory and game theory, Wald's maximin model is a non-probabilistic decision-making model according to which decisions are ranked on the basis of their worst-case outcomes.
In decision theory and game theory, Wald's maximin model is a non-probabilistic decision-making model according to which decisions are ranked on the basis of their worst-case outcomes.
Walrasian auction
A Walrasian auction, introduced by Leon Walras, is a type of simultaneous auction where each agent calculates its demand for the good at every possible price and submits this to an auctioneer.
A Walrasian auction, introduced by Leon Walras, is a type of simultaneous auction where each agent calculates its demand for the good at every possible price and submits this to an auctioneer.
Weak duality
In applied mathematics, weak duality is a concept in optimization which states that the solution to the primal problem is always greater than or equal to the solution to an associated dual p...
In applied mathematics, weak duality is a concept in optimization which states that the solution to the primal problem is always greater than or equal to the solution to an associated dual p...
Wing shape optimization
Wing shape optimization is a software implementation of shape optimization primarily used for aircraft design.
Wing shape optimization is a software implementation of shape optimization primarily used for aircraft design.
Wing-shape optimization
Wing-shape optimization is a software implementation of shape optimization primarily used for aircraft design.
Wing-shape optimization is a software implementation of shape optimization primarily used for aircraft design.
Wolfe conditions
In the unconstrained minimization problem, the Wolfe conditions are a set of inequalities for performing inexact line search, especially in quasi-Newton methods.
In the unconstrained minimization problem, the Wolfe conditions are a set of inequalities for performing inexact line search, especially in quasi-Newton methods.
Wolfe duality
In mathematical optimization, Wolfe duality is type of dual problem in which the objective function and constraints are all differentiable functions.
In mathematical optimization, Wolfe duality is type of dual problem in which the objective function and constraints are all differentiable functions.