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Voting and verification process using a cast-as-intended verifiability approach with a second device
Voting and verification process using a cast-as-intended verifiability approach with a second device
Tobias Hilt, Benjamin Berens, Tomasz Truderung, Margarita Udovychenko, Stephan Neumann …
„Wie können mittelständische Unternehmen die Datenanalyse gewinnbringend einsetzen?" – Die Data-driven Society ist Thema der Veranstaltungsreihe Fokus: Zukunft. Unser Leben 2050 am 21. November 2024. - Campus-Report am 20.11.2024
„Wie können mittelständische Unternehmen die Datenanalyse gewinnbringend einsetzen?" – Die Data-driven Society ist Thema der Veranstaltungsreihe Fokus: Zukunft. Unser Leben 2050 am 21. November 2024. - Campus-Report am 20.11.2024
Stefan Fuchs
„Ein teurer Irrweg“ – KIT Studie zeigt: energieautarke Eigenheime sind keine Strategie für die Energiewende in Europa - Campus-Report am 26.12.2023
„Ein teurer Irrweg“ – KIT Studie zeigt: energieautarke Eigenheime sind keine Strategie für die Energiewende in Europa - Campus-Report am 26.12.2023
Stefan Fuchs
„Charisma kann man lernen“ – KIT-Expertin für Personalentwicklung zum Führungsstil in der digitalen Arbeitswelt der Zukunft. - Campus-Report am 28.11.2023
„Charisma kann man lernen“ – KIT-Expertin für Personalentwicklung zum Führungsstil in der digitalen Arbeitswelt der Zukunft. - Campus-Report am 28.11.2023
Stefan Fuchs
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
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
Computational Economics WS 2022/23, Teil 1
Computational Economics WS 2022/23, Teil 1
Pradyumn Shukla
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
„Faktenbasierte Orientierung in Zeiten multipler Krisen“ – Das KIT-Zentrum Mensch und Technik - Campus-Report am 27.09.2022
„Faktenbasierte Orientierung in Zeiten multipler Krisen“ – Das KIT-Zentrum Mensch und Technik - Campus-Report am 27.09.2022
Stefan Fuchs
Nudging - Stupsen und Schubsen
Nudging - Stupsen und Schubsen
Moritz Chelius
„Hilft die Lebensmittelampel sich gesünder zu ernähren?“ – Eine Studie unter Mitwirkung des KIT untersucht die Wirkungen des Gesundheits-Nudging. - Campus-Report am 28.06.2022
„Hilft die Lebensmittelampel sich gesünder zu ernähren?“ – Eine Studie unter Mitwirkung des KIT untersucht die Wirkungen des Gesundheits-Nudging. - Campus-Report am 28.06.2022
Stefan Fuchs
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
„Standortvorteil durch Quantencomputing“ – die Vortragsreihe „Fokus: Zukunft. Unser Leben 2050.“ thematisiert die zweite Quantenrevolution - Campus-Report am 17.05.2022
„Standortvorteil durch Quantencomputing“ – die Vortragsreihe „Fokus: Zukunft. Unser Leben 2050.“ thematisiert die zweite Quantenrevolution - Campus-Report am 17.05.2022
Stefan Fuchs
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
BMBF gefördertes NOLAN Projekt stellt sich vor : Skalierbare Notfall-Logistik für urbane Räume als öffentlich-private Partnerschaft im Katastrophenfall
BMBF gefördertes NOLAN Projekt stellt sich vor : Skalierbare Notfall-Logistik für urbane Räume als öffentlich-private Partnerschaft im Katastrophenfall
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