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Matematisk programmering

58 metoder i denne familie.

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Afrikansk Gribbe OptimeringsalgoritmeThe African Vultures Optimization Algorithm (AVOA) is a metaheuristic algorithm introduced by Moghdani and Salimifard in 2020, inspired by the search and scavenging behavior of AfrAgent-Based Dynamic ProgrammingAgent-based dynamic programming (ABDP) embeds Bellman's dynamic programming framework within individual agents of an agent-based model, enabling each agent to solve sequential, mulAgent-baseret målprogrammeringAgent-Based Goal Programming (ABGP) integrates agent-based simulation with goal programming optimization to model systems where multiple autonomous decision-makers pursue competingAgent-Based Integer ProgrammingAgent-Based Integer Programming (ABIP) couples the behavioral richness of agent-based modeling with the combinatorial rigor of integer programming. Individual agents pursue local oAquila OptimizerThe Aquila Optimizer (AO) is a nature-inspired metaheuristic algorithm presented by Abualigah et al. in 2021, modeled after the hunting behavior and sensory abilities of golden eagAritmetisk OptimeringsalgoritmeThe Arithmetic Optimization Algorithm (AOA) is a metaheuristic optimization approach introduced by Abualigah et al. in 2020 that leverages mathematical operators (multiplication, d

Reading path

This topic's most-referenced foundational methods, in the order they were developed — a place to start if you're new here.

  1. Lineær programmering1947by George B. Dantzig
  2. Stokastisk Lineær Programmering1955by George B. Dantzig
  3. Dynamisk Programmering1957by Richard Bellman
  4. Stokastisk dynamisk programmering1957by Bellman, R.; formalized for stochastic settings by Puterman, M. L.
  5. Heltalsprogrammering1958by Ralph Gomory (cutting planes, 1958); land-and-doig branch-and-bound (1960)
  6. Blandet-heltallig programmering1958–1960by Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960)
  7. Bayesiansk optimering1975 (foundational); 2012 (ML standard)by Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)
  8. Slimskimmelsalgoritmen2020by Shimin Li
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Alle metoder 58

Afrikansk Gribbe OptimeringsalgoritmeAgent-Based Dynamic ProgrammingAgent-baseret målprogrammeringAgent-Based Integer ProgrammingAquila OptimizerAritmetisk OptimeringsalgoritmeBayesiansk dynamisk programmeringBayesiansk målprogrammeringBayesiansk heltalsoptimeringBayesiansk lineær programmeringBayesiansk HeltalsprogrammeringBayesiansk optimeringDobbelt niveau optimering (leder-følger)Branch and BoundConstraint ProgrammingKonveks optimeringDeterministisk Dynamisk ProgrammeringDeterministisk heltalsoptimeringDeterministisk Lineær ProgrammeringDeterministisk Mixed-Integer ProgrammingDværgmangustoptimeringDynamisk ProgrammeringEvolutionary Strategy (CMA-ES)Hamilton-Jacobi-Bellman-ligningenHarris Hawks OptimizationHonninggrævling-algoritmenHeltalsprogrammeringJellyfish Search OptimizerLineær programmeringBlandet-heltallig programmeringMulti-Objektiv Dynamisk ProgrammeringMulti-Objective Goal ProgrammingMulti-Objektiv Lineær Programmering (MOLP)Multi-Objective Mixed-Integer ProgrammingIkke-lineær programmeringPolicy Scenario Dynamic ProgrammingPolicy Scenario Goal ProgrammingPolicy Scenario Integer ProgrammingKvadratisk programmering (QP)Robust MålprogrammeringRobust Integer ProgrammingRobust Lineær ProgrammeringRobust Mixed-Integer ProgrammingRobust optimeringRunge Kutta OptimeringsalgoritmeSimplexmetodenSlimskimmelsalgoritmenStokastisk dynamisk programmeringStokastisk MålprogrammeringStokastisk HeltalsprogrammeringStokastisk Lineær ProgrammeringStokastisk HeltalsprogrammeringStokastisk optimeringSurrogatbaseret optimeringUnit CommitmentVariable Neighborhood Search (VNS)Køretøjsruteplanlægningsproblem (VRP)Whale Optimization Algorithm (WOA)