KITopen-Medienportal

KIT-Bibliothek

KIT-Bibliothek Medienportal

FAQ Audio-/Videodatei publizieren

Vorlesung

bwGPT Update September 24

bwGPT Update September 24

Sarah Holstein

bwGPT Update September 24

bwGPT Update September 24

Sarah Holstein

Nature-inspired optimization methods SS 2023, Teil 16

Nature-inspired optimization methods SS 2023, Teil 16

Pradyumn Shukla

Nature-inspired optimization methods SS 2023, Teil 15

Nature-inspired optimization methods SS 2023, Teil 15

Pradyumn Shukla

Nature-inspired optimization methods SS 2023, Teil 14

Nature-inspired optimization methods SS 2023, Teil 14

Pradyumn Shukla

Nature-inspired optimization methods SS 2023

Nature-inspired optimization methods SS 2023

Pradyumn Shukla

Nature-inspired optimization methods SS 2023, Teil 13

Nature-inspired optimization methods SS 2023, Teil 13

Pradyumn Shukla

Nature-inspired optimization methods SS 2023, Teil 12

Nature-inspired optimization methods SS 2023, Teil 12

Pradyumn Shukla

Nature-inspired optimization methods SS 2023, Teil 11

Nature-inspired optimization methods SS 2023, Teil 11

Pradyumn Shukla

Nature-inspired optimization methods SS 2023, Teil 10

Nature-inspired optimization methods SS 2023, Teil 10

Pradyumn Shukla

Nature-inspired optimization methods SS 2023, Teil 9

Nature-inspired optimization methods SS 2023, Teil 9

Pradyumn Shukla

Nature-inspired optimization methods SS 2023, Teil 8

Nature-inspired optimization methods SS 2023, Teil 8

Pradyumn Shukla

Nature-inspired optimization methods SS 2023, Teil 7

Nature-inspired optimization methods SS 2023, Teil 7

Pradyumn Shukla

Nature-inspired optimization methods SS 2023, Teil 6

Nature-inspired optimization methods SS 2023, Teil 6

Pradyumn Shukla

Nature-inspired optimization methods SS 2023, Teil 1

Nature-inspired optimization methods SS 2023, Teil 1

Pradyumn Shukla

Nature-inspired optimization methods SS 2023, Teil 2

Nature-inspired optimization methods SS 2023, Teil 2

Pradyumn Shukla

Nature-inspired optimization methods SS 2023, Teil 5

Nature-inspired optimization methods SS 2023, Teil 5

Pradyumn Shukla

Nature-inspired optimization methods SS 2023, Teil 4

Nature-inspired optimization methods SS 2023, Teil 4

Pradyumn Shukla

Nature-inspired optimization methods SS 2023, Teil 3

Nature-inspired optimization methods SS 2023, Teil 3

Pradyumn Shukla

Computational Economics WS 2022/23

Computational Economics WS 2022/23

Pradyumn Shukla

Computational Economics WS 2022/23, Teil 12

Computational Economics WS 2022/23, Teil 12

Pradyumn Shukla

Homo oeconomicus - Problem oder Lösung?

Homo oeconomicus - Problem oder Lösung?

Dirk Specht

Computational Economics WS 2022/23, Teil 8

Computational Economics WS 2022/23, Teil 8

Pradyumn Shukla

Computational Economics WS 2022/23, Teil 7

Computational Economics WS 2022/23, Teil 7

Pradyumn Shukla

Computational Economics WS 2022/23, Teil 5

Computational Economics WS 2022/23, Teil 5

Pradyumn Shukla

Computational Economics WS 2022/23, Teil 4

Computational Economics WS 2022/23, Teil 4

Pradyumn Shukla

Computational Economics WS 2022/23, Teil 10

Computational Economics WS 2022/23, Teil 10

Pradyumn Shukla

Computational Economics WS 2022/23, Teil 9

Computational Economics WS 2022/23, Teil 9

Pradyumn Shukla

Computational Economics WS 2022/23, Teil 6

Computational Economics WS 2022/23, Teil 6

Pradyumn Shukla

Computational Economics WS 2022/23, Teil 3

Computational Economics WS 2022/23, Teil 3

Pradyumn Shukla

Computational Economics WS 2022/23, Teil 2

Computational Economics WS 2022/23, Teil 2

Pradyumn Shukla

Computational Economics WS 2022/23, Teil 11

Computational Economics WS 2022/23, Teil 11

Pradyumn Shukla

Filmische Inszenierung und Aushandlung von KI am Beispiel von Ex Machina und Real Humans

Filmische Inszenierung und Aushandlung von KI am Beispiel von Ex Machina und Real Humans

Marie-Hélène Adam

Computational Economics WS 2022/23, Teil 1

Computational Economics WS 2022/23, Teil 1

Pradyumn Shukla

Wissenschafts-PR

Wissenschafts-PR

Annette Leßmöllmann

Wissenschaftsjournalismus

Wissenschaftsjournalismus

Annette Leßmöllmann

Mixed-Integer Optimization II, Section 3.8.3 (Feasibility pump), Section 3.8.4 (Metaheuristics)

Mixed-Integer Optimization II, Section 3.8.3 (Feasibility pump), Section 3.8.4 (Metaheuristics)

Oliver Stein

Mixed-Integer Optimization II, Section 3.7.3 (Pricing)

Mixed-Integer Optimization II, Section 3.7.3 (Pricing)

Oliver Stein

Mixed-Integer Optimization II, Section 3.7.1 (Dantzig-Wolfe decomposition, motivation and construction)

Mixed-Integer Optimization II, Section 3.7.1 (Dantzig-Wolfe decomposition, motivation and construction)

Oliver Stein

Mixed-Integer Optimization II, Section 3.6.4 (A subgradient method)

Mixed-Integer Optimization II, Section 3.6.4 (A subgradient method)

Oliver Stein

Mixed-Integer Optimization II, Section 3.4 (Generalized Benders decomposition, Introduction)

Mixed-Integer Optimization II, Section 3.4 (Generalized Benders decomposition, Introduction)

Oliver Stein

Mixed-Integer Optimization II, Section 3.6.1 (Lagrange relaxation, construction and relaxation property)

Mixed-Integer Optimization II, Section 3.6.1 (Lagrange relaxation, construction and relaxation property)

Oliver Stein

Mixed-Integer Optimization II, Section 3.5.4 (Cutting plane method for MICP), part 2

Mixed-Integer Optimization II, Section 3.5.4 (Cutting plane method for MICP), part 2

Oliver Stein

Mixed-Integer Optimization II, Section 3.5.3 (Optimality cuts)

Mixed-Integer Optimization II, Section 3.5.3 (Optimality cuts)

Oliver Stein

Mixed-Integer Optimization II, Section 3.5.2 (Feasibility cuts)

Mixed-Integer Optimization II, Section 3.5.2 (Feasibility cuts)

Oliver Stein

Mixed-Integer Optimization II, Section 3.8.1 (Diving heuristics), Section 3.8.2 (Local branching)

Mixed-Integer Optimization II, Section 3.8.1 (Diving heuristics), Section 3.8.2 (Local branching)

Oliver Stein

Mixed-Integer Optimization II, Section 3.7.4 (Initialization)

Mixed-Integer Optimization II, Section 3.7.4 (Initialization)

Oliver Stein

Mixed-Integer Optimization II, Section 3.7.2 (Column generation)

Mixed-Integer Optimization II, Section 3.7.2 (Column generation)

Oliver Stein

Mixed-Integer Optimization II, Section 3.6.3 (Comparison to continuous relaxation)

Mixed-Integer Optimization II, Section 3.6.3 (Comparison to continuous relaxation)

Oliver Stein

Mixed-Integer Optimization II, Section 3.6.2 (Decomposition by Lagrange relaxation)

Mixed-Integer Optimization II, Section 3.6.2 (Decomposition by Lagrange relaxation)

Oliver Stein

Mixed-Integer Optimization II, Section 3.5.4 (Cutting plane method for MICP), part 1

Mixed-Integer Optimization II, Section 3.5.4 (Cutting plane method for MICP), part 1

Oliver Stein

Mixed-Integer Optimization II, Section 3.5.1 (Motivation of the Outer Approximation method)

Mixed-Integer Optimization II, Section 3.5.1 (Motivation of the Outer Approximation method)

Oliver Stein

Mixed-Integer Optimization II, Section 3.2 (Continuous relaxation and rounding)

Mixed-Integer Optimization II, Section 3.2 (Continuous relaxation and rounding)

Oliver Stein

Mixed-Integer Optimization II, Section 3.4.7 (Cutting plane method for MICP), part 2

Mixed-Integer Optimization II, Section 3.4.7 (Cutting plane method for MICP), part 2

Oliver Stein

Mixed-Integer Optimization II, Section 3.4.7 (Cutting plane method for MICP), part 1

Mixed-Integer Optimization II, Section 3.4.7 (Cutting plane method for MICP), part 1

Oliver Stein

Mixed-Integer Optimization II, Section 3.4.6 (Feasibility cuts)

Mixed-Integer Optimization II, Section 3.4.6 (Feasibility cuts)

Oliver Stein

Mixed-Integer Optimization II, Section 3.4.5 (Optimality cuts), part 3

Mixed-Integer Optimization II, Section 3.4.5 (Optimality cuts), part 3

Oliver Stein

Mixed-Integer Optimization II, Section 3.4.5 (Optimality cuts), part 2

Mixed-Integer Optimization II, Section 3.4.5 (Optimality cuts), part 2

Oliver Stein

Mixed-Integer Optimization II, Section 3.4.3 (A functional description of the reduced feasible set)

Mixed-Integer Optimization II, Section 3.4.3 (A functional description of the reduced feasible set)

Oliver Stein

Mixed-Integer Optimization II, Section 3.4.4 (A purely discrete problem equivalent to MICP)

Mixed-Integer Optimization II, Section 3.4.4 (A purely discrete problem equivalent to MICP)

Oliver Stein

Mixed-Integer Optimization II, Section 3.3.3 (The nonconvex case)

Mixed-Integer Optimization II, Section 3.3.3 (The nonconvex case)

Oliver Stein

Mixed-Integer Optimization II, Section 3.1 (Models in portfolio optimization)

Mixed-Integer Optimization II, Section 3.1 (Models in portfolio optimization)

Oliver Stein

Mixed-Integer Optimization II, Section 3 (Mixed-integer nonlinear optimization), Introduction

Mixed-Integer Optimization II, Section 3 (Mixed-integer nonlinear optimization), Introduction

Oliver Stein

Mixed-Integer Optimization II, Section 3.4.5 (Optimality cuts), part 1

Mixed-Integer Optimization II, Section 3.4.5 (Optimality cuts), part 1

Oliver Stein

Mixed-Integer Optimization II, Section 3.4.1 (Reduced feasible set and reduced objective function)

Mixed-Integer Optimization II, Section 3.4.1 (Reduced feasible set and reduced objective function)

Oliver Stein

Mixed-Integer Optimization II, Section 3.4.2 (Convexity of the reduced feasible set and of the reduced objective function)

Mixed-Integer Optimization II, Section 3.4.2 (Convexity of the reduced feasible set and of the reduced objective function)

Oliver Stein

Mixed-Integer Optimization II, Section 3.3.1 (B&B für MINLP - A conceptual algorithm), Section 3.3.2 (The convex case)

Mixed-Integer Optimization II, Section 3.3.1 (B&B für MINLP - A conceptual algorithm), Section 3.3.2 (The convex case)

Oliver Stein

Nature-inspired optimization methods SS 2022 : Chapter: Evolutionary algorithms

Nature-inspired optimization methods SS 2022 : Chapter: Evolutionary algorithms

Pradyumn Shukla

Nature-inspired optimization methods SS 2022 : Modelling fairness in resource allocation problems

Nature-inspired optimization methods SS 2022 : Modelling fairness in resource allocation problems

Pradyumn Shukla

Nature-inspired optimization methods SS 2022 : Chapter: Constraint handling techniques

Nature-inspired optimization methods SS 2022 : Chapter: Constraint handling techniques

Pradyumn Shukla

Nature-inspired optimization methods SS 2022 : User preferences, distributed computing

Nature-inspired optimization methods SS 2022 : User preferences, distributed computing

Pradyumn Shukla

Nature-inspired optimization methods SS 2022 : Chapter: An Algorithmic Framework for Many-Objective-Optimization

Nature-inspired optimization methods SS 2022 : Chapter: An Algorithmic Framework for Many-Objective-Optimization

Pradyumn Shukla

Nature-inspired optimization methods SS 2022 : Robust Design Optimization

Nature-inspired optimization methods SS 2022 : Robust Design Optimization

Pradyumn Shukla

Nature-inspired optimization methods SS 2022 : Multi-objective Evolutionary Algorithms

Nature-inspired optimization methods SS 2022 : Multi-objective Evolutionary Algorithms

Pradyumn Shukla

Nature-inspired optimization methods SS 2022 : Search operators

Nature-inspired optimization methods SS 2022 : Search operators

Pradyumn Shukla

Nature-inspired optimization methods SS 2022 : Chapter: Representation

Nature-inspired optimization methods SS 2022 : Chapter: Representation

Pradyumn Shukla

Nature-inspired optimization methods SS 2022 : Chapter: Escaping Local Minima

Nature-inspired optimization methods SS 2022 : Chapter: Escaping Local Minima

Pradyumn Shukla

Nature-inspired optimization methods SS 2022 : Problem Solving and Basic Concepts II. Basic Concepts

Nature-inspired optimization methods SS 2022 : Problem Solving and Basic Concepts II. Basic Concepts

Pradyumn Shukla

Nature-inspired optimization methods SS 2022 : Chapter: Escaping Local Minima II. Tabu Search

Nature-inspired optimization methods SS 2022 : Chapter: Escaping Local Minima II. Tabu Search

Pradyumn Shukla

Nature-inspired optimization methods SS 2022 : Chapter 1: Problem Solving and Basic Concepts

Nature-inspired optimization methods SS 2022 : Chapter 1: Problem Solving and Basic Concepts

Pradyumn Shukla

Nature-inspired optimization methods SS 2022 : Introductory lecture

Nature-inspired optimization methods SS 2022 : Introductory lecture

Pradyumn Shukla

Seminar on IPCC Assessment Report

Seminar on IPCC Assessment Report

Joaquim Pinto, Patrick Ludwig

Climate Modelling and Dynamics with ICON

Climate Modelling and Dynamics with ICON

Joaquim G. Pinto, Patrick Ludwig, Aiko Voigt

Parametric Optimization, Section 2.3.2 (Three regularity conditions)

Parametric Optimization, Section 2.3.2 (Three regularity conditions)

Oliver Stein

Parametric Optimization, Section 2.3.1 (Criticality notions for constrained problems)

Parametric Optimization, Section 2.3.1 (Criticality notions for constrained problems)

Oliver Stein

Parametric Optimization, Section 2.2.3 (Quadratic indices), Section 2.2.4 (Stability of nondegenerate minimal points)

Parametric Optimization, Section 2.2.3 (Quadratic indices), Section 2.2.4 (Stability of nondegenerate minimal points)

Oliver Stein

Parametric Optimization, Section 1.9 (Counter examples)

Parametric Optimization, Section 1.9 (Counter examples)

Oliver Stein

Parametric Optimization, Section 1.3 (Algorithms and a fourth key question), Section 1.4 (Decomposition methods), Section 1.5 (Robust and semi-infinite optimization)

Parametric Optimization, Section 1.3 (Algorithms and a fourth key question), Section 1.4 (Decomposition methods), Section 1.5 (Robust and semi-infinite optimization)

Oliver Stein

Parametric Optimization, Section 1.1 (Shadow prices and three key questions), Section 1.2 (Hotelling's lemma and the same questions)

Parametric Optimization, Section 1.1 (Shadow prices and three key questions), Section 1.2 (Hotelling's lemma and the same questions)

Oliver Stein

Parametric Optimization

Parametric Optimization

Oliver Stein

Parametric Optimization, Section 3.2.2 (Applications and characterizations for semicontinuity properties of functions)

Parametric Optimization, Section 3.2.2 (Applications and characterizations for semicontinuity properties of functions)

Oliver Stein

Parametric Optimization, Section 2.3.7 (Sensitivity of nondegenerate minimal values), part 2

Parametric Optimization, Section 2.3.7 (Sensitivity of nondegenerate minimal values), part 2

Oliver Stein

Parametric Optimization, Section 2.3.5 (Stability of nondegenerate minimal points)

Parametric Optimization, Section 2.3.5 (Stability of nondegenerate minimal points)

Oliver Stein

Parametric Optimization, Section 2.3.7 (Sensitivity of nondegenerate minimal values), part 1

Parametric Optimization, Section 2.3.7 (Sensitivity of nondegenerate minimal values), part 1

Oliver Stein

Parametric Optimization, Section 4.1.3 (Optimality conditions)

Parametric Optimization, Section 4.1.3 (Optimality conditions)

Oliver Stein

Parametric Optimization, Section 4.1.4 (Reduction Ansatz)

Parametric Optimization, Section 4.1.4 (Reduction Ansatz)

Oliver Stein

Parametric Optimization, Section 3.7 (Directional sensitivity of minimal values)

Parametric Optimization, Section 3.7 (Directional sensitivity of minimal values)

Oliver Stein

Global Optimization I, Section 2.8.5 (Frank-Wolfe method), part 3

Global Optimization I, Section 2.8.5 (Frank-Wolfe method), part 3

Oliver Stein

Global Optimization I, Section 2.8.5 (Frank-Wolfe method), part 2

Global Optimization I, Section 2.8.5 (Frank-Wolfe method), part 2

Oliver Stein

Global Optimization I, Section 2.8.5 (Frank-Wolfe method), part 1

Global Optimization I, Section 2.8.5 (Frank-Wolfe method), part 1

Oliver Stein

Global Optimization I, Section 2.8.4 (Kelley's cutting plane method), part 1

Global Optimization I, Section 2.8.4 (Kelley's cutting plane method), part 1

Oliver Stein

Global Optimization I, Section 2.8.4 (Kelley's cutting plane method), part 2

Global Optimization I, Section 2.8.4 (Kelley's cutting plane method), part 2

Oliver Stein

Global Optimization I, Section 2.2.1 (Higher-dimensional first derivatives)

Global Optimization I, Section 2.2.1 (Higher-dimensional first derivatives)

Oliver Stein

Global Optimization I, Section 2.1 (Convexity), part 2

Global Optimization I, Section 2.1 (Convexity), part 2

Oliver Stein

Global Optimization I, Section 1.2.4 (Unbounded feasible sets), part 2

Global Optimization I, Section 1.2.4 (Unbounded feasible sets), part 2

Oliver Stein

Global Optimization I, Section 1.2.1 (Definition of solvability)

Global Optimization I, Section 1.2.1 (Definition of solvability)

Oliver Stein

Global Optimization I, Section 2.8 (Numerical methods); Section 2.8.1 (Basic idea of the gradient method)

Global Optimization I, Section 2.8 (Numerical methods); Section 2.8.1 (Basic idea of the gradient method)

Oliver Stein

Global Optimization I, Section 2.7.2 (The KKT conditions)

Global Optimization I, Section 2.7.2 (The KKT conditions)

Oliver Stein

Global Optimization I, Section 2.7.1 (Lagrange- and Wolfe-duality), part 3

Global Optimization I, Section 2.7.1 (Lagrange- and Wolfe-duality), part 3

Oliver Stein

Global Optimization I, Section 2.7.1 (Lagrange- and Wolfe-duality), part 2

Global Optimization I, Section 2.7.1 (Lagrange- and Wolfe-duality), part 2

Oliver Stein

Global Optimization I, Section 2.8.6 (Basic idea of primal-dual IPMs), part 3

Global Optimization I, Section 2.8.6 (Basic idea of primal-dual IPMs), part 3

Oliver Stein

Global Optimization I, Section 2.8.6 (Basic idea of primal-dual IPMs), part 2

Global Optimization I, Section 2.8.6 (Basic idea of primal-dual IPMs), part 2

Oliver Stein

Global Optimization I, Section 2.8.6 (Basic idea of primal-dual IPMs), part 1

Global Optimization I, Section 2.8.6 (Basic idea of primal-dual IPMs), part 1

Oliver Stein

Global Optimization I, Section 2.8.3 (Basic idea of cutting plane methods)

Global Optimization I, Section 2.8.3 (Basic idea of cutting plane methods)

Oliver Stein

Global Optimization I, Section 2.8.2 (Basic idea of Newton's method)

Global Optimization I, Section 2.8.2 (Basic idea of Newton's method)

Oliver Stein

Global Optimization I, Section 2.7.5 (Constraint Qualifications)

Global Optimization I, Section 2.7.5 (Constraint Qualifications)

Oliver Stein

Parametric Optimization, Section 2.2.2 (Nondegenerate critical points)

Parametric Optimization, Section 2.2.2 (Nondegenerate critical points)

Oliver Stein

Parametric Optimization, Section 2.2.1 (Preliminary thoughts)

Parametric Optimization, Section 2.2.1 (Preliminary thoughts)

Oliver Stein

Parametric Optimization, Section 2.1 (Basic terminology and notation)

Parametric Optimization, Section 2.1 (Basic terminology and notation)

Oliver Stein

Parametric Optimization, Section 1.6 (Bilevel optimization), Section 1.7 (Nash games), Section 1.8 (Multiobjective optimization)

Parametric Optimization, Section 1.6 (Bilevel optimization), Section 1.7 (Nash games), Section 1.8 (Multiobjective optimization)

Oliver Stein