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2021 Fakultät für Wirtschaftswissenschaften (WIWI)

„Mehr als nur Programmieren“ - Eine KI-Werkstatt am KIT bildet Studierende für praktische Anwendungen von künstlicher Intelligenz aus. - Campus-Report am 21.12.2021

„Mehr als nur Programmieren“ - Eine KI-Werkstatt am KIT bildet Studierende für praktische Anwendungen von künstlicher Intelligenz aus. - Campus-Report am 21.12.2021

Stefan Fuchs

„Investieren fürs Gemeinwohl“ – Nachhaltige Geldanlagen sind diese Woche Thema des „Fokus: Zukunft. Unser Leben 2050.“ - Campus-Report am 23.11.2021

„Investieren fürs Gemeinwohl“ – Nachhaltige Geldanlagen sind diese Woche Thema des „Fokus: Zukunft. Unser Leben 2050.“ - Campus-Report am 23.11.2021

Stefan Fuchs

Computational Economics WS 2021/2022, 17.11.2021

Computational Economics WS 2021/2022, 17.11.2021

Pradyumn Shukla

Computational Economics WS 2021/2022, 15.11.2021

Computational Economics WS 2021/2022, 15.11.2021

Pradyumn Shukla

Computational Economics WS 2021/2022, 15.11.2021

Computational Economics WS 2021/2022, 15.11.2021

Pradyumn Shukla

Computational Economics WS 2021/2022, 12.11.2021

Computational Economics WS 2021/2022, 12.11.2021

Pradyumn Shukla

Computational Economics WS 2021/2022, 11.11.2021

Computational Economics WS 2021/2022, 11.11.2021

Pradyumn Shukla

Computational Economics WS 2021/2022, 10.11.2021

Computational Economics WS 2021/2022, 10.11.2021

Pradyumn Shukla

Computational Economics WS 2021/2022, 16.11.2021

Computational Economics WS 2021/2022, 16.11.2021

Pradyumn Shukla

Computational Economics WS 2021/2022, 15.11.2021

Computational Economics WS 2021/2022, 15.11.2021

Pradyumn Shukla

Computational Economics WS 2021/2022, 16.11.2021

Computational Economics WS 2021/2022, 16.11.2021

Pradyumn Shukla

Computational Economics WS 2021/2022, 09.11.2021

Computational Economics WS 2021/2022, 09.11.2021

Pradyumn Shukla

Computational Economics WS 2021/2022, 08.11.2021

Computational Economics WS 2021/2022, 08.11.2021

Pradyumn Shukla

Nonlinear Optimization I, Introduction

Nonlinear Optimization I, Introduction

Oliver Stein

Nonlinear Optimization II, Section 3.1.2 (First order approximations of the feasible set), part 2

Nonlinear Optimization II, Section 3.1.2 (First order approximations of the feasible set), part 2

Oliver Stein

Nonlinear Optimization I, Section 2.1.4 - bonus track on eigenvalues

Nonlinear Optimization I, Section 2.1.4 - bonus track on eigenvalues

Oliver Stein

Nonlinear Optimization II, Section 3.3.3 (Barrier method), part 1

Nonlinear Optimization II, Section 3.3.3 (Barrier method), part 1

Oliver Stein

Nonlinear Optimization II, Section 3.2.10 (Convex optimization problems)

Nonlinear Optimization II, Section 3.2.10 (Convex optimization problems)

Oliver Stein

Nonlinear Optimization II, Section 3.2.09 (Second order optimality conditions), part 1

Nonlinear Optimization II, Section 3.2.09 (Second order optimality conditions), part 1

Oliver Stein

Nonlinear Optimization II, Section 3.3.6 (Merit functions and filters), part 2

Nonlinear Optimization II, Section 3.3.6 (Merit functions and filters), part 2

Oliver Stein

Nonlinear Optimization II, Section 3.3.5 (SQP methods), part 1

Nonlinear Optimization II, Section 3.3.5 (SQP methods), part 1

Oliver Stein

Nonlinear Optimization II, Section 3.3.3 (Barrier method), part 2

Nonlinear Optimization II, Section 3.3.3 (Barrier method), part 2

Oliver Stein

Nonlinear Optimization II, Section 3.3.2 (Multiplier method)

Nonlinear Optimization II, Section 3.3.2 (Multiplier method)

Oliver Stein

Nonlinear Optimization II, Section 3.3.1 (Penalty methods)

Nonlinear Optimization II, Section 3.3.1 (Penalty methods)

Oliver Stein

Nonlinear Optimization II, Section 3.2.09 (Second order optimality conditions), part 2

Nonlinear Optimization II, Section 3.2.09 (Second order optimality conditions), part 2

Oliver Stein

Nonlinear Optimization II, Section 3.2.08 (Karush-Kuhn-Tucker points), part 2

Nonlinear Optimization II, Section 3.2.08 (Karush-Kuhn-Tucker points), part 2

Oliver Stein

Nonlinear Optimization I, Section 1.1 (Examples and notions), part 1

Nonlinear Optimization I, Section 1.1 (Examples and notions), part 1

Oliver Stein

Nonlinear Optimization II, Section 3.3.6 (Merit functions and filters), part 1

Nonlinear Optimization II, Section 3.3.6 (Merit functions and filters), part 1

Oliver Stein

Nonlinear Optimization II, Section 3.3.5 (SQP methods), part 2

Nonlinear Optimization II, Section 3.3.5 (SQP methods), part 2

Oliver Stein

Nonlinear Optimization II, Section 3.3.4 (Primal-dual interior point methods)

Nonlinear Optimization II, Section 3.3.4 (Primal-dual interior point methods)

Oliver Stein

Nonlinear Optimization I, Section 2.1.2 (First order optimality condition), part 2

Nonlinear Optimization I, Section 2.1.2 (First order optimality condition), part 2

Oliver Stein

Nonlinear Optimization I, Section 2.1.1 (Descent directions)

Nonlinear Optimization I, Section 2.1.1 (Descent directions)

Oliver Stein

Nonlinear Optimization I, Section 1.2 (Solvability), part 1

Nonlinear Optimization I, Section 1.2 (Solvability), part 1

Oliver Stein

Nonlinear Optimization I, Section 1.1 (Examples and notions), part 2

Nonlinear Optimization I, Section 1.1 (Examples and notions), part 2

Oliver Stein

Nonlinear Optimization I, Section 2.2.04 (Variable metric methods), part 2

Nonlinear Optimization I, Section 2.2.04 (Variable metric methods), part 2

Oliver Stein

Nonlinear Optimization I, Section 2.2.04 (Variable metric methods), part 1

Nonlinear Optimization I, Section 2.2.04 (Variable metric methods), part 1

Oliver Stein

Nonlinear Optimization I, Section 2.2.03 (Gradient method), part 1

Nonlinear Optimization I, Section 2.2.03 (Gradient method), part 1

Oliver Stein

Nonlinear Optimization I, Section 2.2.02 (Step size control), part 1

Nonlinear Optimization I, Section 2.2.02 (Step size control), part 1

Oliver Stein

Nonlinear Optimization I, Section 2.1.4 (Second order optimality conditions), part 3

Nonlinear Optimization I, Section 2.1.4 (Second order optimality conditions), part 3

Oliver Stein

Nonlinear Optimization I, Section 2.1.4 (Second order optimality conditions), part 2 (edit)

Nonlinear Optimization I, Section 2.1.4 (Second order optimality conditions), part 2 (edit)

Oliver Stein

Nonlinear Optimization II, Section 3.1.1 (Topological properties), part 1

Nonlinear Optimization II, Section 3.1.1 (Topological properties), part 1

Oliver Stein

Nonlinear Optimization I, Section 2.2.10 (Trust region methods)

Nonlinear Optimization I, Section 2.2.10 (Trust region methods)

Oliver Stein

Nonlinear Optimization I, Section 2.2.08 (Conjugate directions)

Nonlinear Optimization I, Section 2.2.08 (Conjugate directions)

Oliver Stein

Nonlinear Optimization I, Section 2.2.07 (Quasi-Newton methods)

Nonlinear Optimization I, Section 2.2.07 (Quasi-Newton methods)

Oliver Stein

Nonlinear Optimization I, Section 2.2.06 (Superlinear convergence)

Nonlinear Optimization I, Section 2.2.06 (Superlinear convergence)

Oliver Stein

Nonlinear Optimization I, Section 2.2.05 - bonus track on Newton's method

Nonlinear Optimization I, Section 2.2.05 - bonus track on Newton's method

Oliver Stein

Nonlinear Optimization II, Section 3.2.06 (Normal cones)

Nonlinear Optimization II, Section 3.2.06 (Normal cones)

Oliver Stein

Nonlinear Optimization II, Section 3.2.05 (Separation theorem), part 2

Nonlinear Optimization II, Section 3.2.05 (Separation theorem), part 2

Oliver Stein

Nonlinear Optimization II, Section 3.2.04 (First order optimality conditions without equality constraints), part 1

Nonlinear Optimization II, Section 3.2.04 (First order optimality conditions without equality constraints), part 1

Oliver Stein

Nonlinear Optimization II, Section 3.2.01 (Stationarity)

Nonlinear Optimization II, Section 3.2.01 (Stationarity)

Oliver Stein

Nonlinear Optimization II, Section 3.2.03 (Theorems of the alternative)

Nonlinear Optimization II, Section 3.2.03 (Theorems of the alternative)

Oliver Stein

Nonlinear Optimization II, Section 3.1.2 (First order approximations of the feasible set), part 3

Nonlinear Optimization II, Section 3.1.2 (First order approximations of the feasible set), part 3

Oliver Stein

Nonlinear Optimization II, Section 3.1.2 (First order approximations of the feasible set), part 1

Nonlinear Optimization II, Section 3.1.2 (First order approximations of the feasible set), part 1

Oliver Stein

Nonlinear Optimization II, Section 3.2.07 (First order optimality conditions with equality constraints), part 1

Nonlinear Optimization II, Section 3.2.07 (First order optimality conditions with equality constraints), part 1

Oliver Stein

Nonlinear Optimization II, Section 3.2.08 (Karush-Kuhn-Tucker points), part 3

Nonlinear Optimization II, Section 3.2.08 (Karush-Kuhn-Tucker points), part 3

Oliver Stein

Nonlinear Optimization II, Section 3.2.08 (Karush-Kuhn-Tucker points), part 1

Nonlinear Optimization II, Section 3.2.08 (Karush-Kuhn-Tucker points), part 1

Oliver Stein

Nonlinear Optimization II, Section 3.2.07 (First order optimality conditions with equality constraints), part 2

Nonlinear Optimization II, Section 3.2.07 (First order optimality conditions with equality constraints), part 2

Oliver Stein

Nonlinear Optimization I, Section 2.2.01 (Descent methods), part 2

Nonlinear Optimization I, Section 2.2.01 (Descent methods), part 2

Oliver Stein

Nonlinear Optimization I, Section 2.2.01 (Descent methods), part 1

Nonlinear Optimization I, Section 2.2.01 (Descent methods), part 1

Oliver Stein

Nonlinear Optimization I, Section 2.1.5 (Convex optimization problems)

Nonlinear Optimization I, Section 2.1.5 (Convex optimization problems)

Oliver Stein

Nonlinear Optimization I, Section 2.1.4 (Second order optimality conditions), part 1

Nonlinear Optimization I, Section 2.1.4 (Second order optimality conditions), part 1

Oliver Stein

Nonlinear Optimization I, Section 2.1.3 (Geometric properties of gradients)

Nonlinear Optimization I, Section 2.1.3 (Geometric properties of gradients)

Oliver Stein

Nonlinear Optimization I, Section 2.1.2 - bonus track on scalar products

Nonlinear Optimization I, Section 2.1.2 - bonus track on scalar products

Oliver Stein

Nonlinear Optimization II, Section 3.2.05 (Separation theorem), part 1

Nonlinear Optimization II, Section 3.2.05 (Separation theorem), part 1

Oliver Stein

Nonlinear Optimization II, Section 3.2.04 (First order optimality conditions without equality constraints), part 2

Nonlinear Optimization II, Section 3.2.04 (First order optimality conditions without equality constraints), part 2

Oliver Stein

Nonlinear Optimization II, Section 3.2.02 (Constraint qualifications)

Nonlinear Optimization II, Section 3.2.02 (Constraint qualifications)

Oliver Stein

Nonlinear Optimization II, Section 3.1.1 (Topological properties), part 2

Nonlinear Optimization II, Section 3.1.1 (Topological properties), part 2

Oliver Stein

Nonlinear Optimization II, Section 3.1 (Properties of the feasible set), Introduction

Nonlinear Optimization II, Section 3.1 (Properties of the feasible set), Introduction

Oliver Stein

Nonlinear Optimization I, Section 2.2.04 (Variable metric methods), part 3

Nonlinear Optimization I, Section 2.2.04 (Variable metric methods), part 3

Oliver Stein

Nonlinear Optimization I, Section 2.2.03 (Gradient method), part 2

Nonlinear Optimization I, Section 2.2.03 (Gradient method), part 2

Oliver Stein

Nonlinear Optimization I, Section 2.2.02 (Step size control), part 2

Nonlinear Optimization I, Section 2.2.02 (Step size control), part 2

Oliver Stein

Nonlinear Optimization I, Section 2.2.01 (Descent methods), part 3

Nonlinear Optimization I, Section 2.2.01 (Descent methods), part 3

Oliver Stein

Nonlinear Optimization I, Section 2.1.2 (First order optimality condition), part 1

Nonlinear Optimization I, Section 2.1.2 (First order optimality condition), part 1

Oliver Stein

Nonlinear Optimization I, Section 1.2 (Solvability), part 2

Nonlinear Optimization I, Section 1.2 (Solvability), part 2

Oliver Stein

Nonlinear Optimization I, Section 2.2.09 (Conjugate gradient methods)

Nonlinear Optimization I, Section 2.2.09 (Conjugate gradient methods)

Oliver Stein

Nonlinear Optimization I, Section 2.2.05 (Newton's methods with and without damping; edit)

Nonlinear Optimization I, Section 2.2.05 (Newton's methods with and without damping; edit)

Oliver Stein

„Die Zeit bis zur Erstversorgung im Krankenhaus ist entscheidend“ – Am Karlsruher Institut für Technologie werden die Rettungsdienste in Baden-Württemberg optimiert - Campus-Report am 21.09.2021

„Die Zeit bis zur Erstversorgung im Krankenhaus ist entscheidend“ – Am Karlsruher Institut für Technologie werden die Rettungsdienste in Baden-Württemberg optimiert - Campus-Report am 21.09.2021

Stefan Fuchs

„Je schöner die Landschaft umso mehr Widerstand gegen Windkraft“ – Eine Studie am Karlsruher Institut für Technologie quantifiziert die Folgen für die Energiewende - Campus-Report am 22.06.2021

„Je schöner die Landschaft umso mehr Widerstand gegen Windkraft“ – Eine Studie am Karlsruher Institut für Technologie quantifiziert die Folgen für die Energiewende - Campus-Report am 22.06.2021

Stefan Fuchs

13: Nature-inspired optimization methods, Vorlesung SS 2021

13: Nature-inspired optimization methods, Vorlesung SS 2021

Pradyumn Shukla

11: Nature-inspired optimization methods, Vorlesung SS 2021

11: Nature-inspired optimization methods, Vorlesung SS 2021

Pradyumn Shukla

10: Nature-inspired optimization methods, Vorlesung SS 2021

10: Nature-inspired optimization methods, Vorlesung SS 2021

Pradyumn Shukla

09: Nature-inspired optimization methods, Vorlesung SS 2021

09: Nature-inspired optimization methods, Vorlesung SS 2021

Pradyumn Shukla

17: Nature-inspired optimization methods, Vorlesung SS 2021

17: Nature-inspired optimization methods, Vorlesung SS 2021

Pradyumn Shukla

16: Nature-inspired optimization methods, Vorlesung SS 2021

16: Nature-inspired optimization methods, Vorlesung SS 2021

Pradyumn Shukla

15: Nature-inspired optimization methods, Vorlesung SS 2021

15: Nature-inspired optimization methods, Vorlesung SS 2021

Pradyumn Shukla

14: Nature-inspired optimization methods, Vorlesung SS 2021

14: Nature-inspired optimization methods, Vorlesung SS 2021

Pradyumn Shukla

12: Nature-inspired optimization methods, Vorlesung SS 2021

12: Nature-inspired optimization methods, Vorlesung SS 2021

Pradyumn Shukla

08: Nature-inspired optimization methods, Vorlesung SS 2021

08: Nature-inspired optimization methods, Vorlesung SS 2021

Pradyumn Shukla

07: Nature-inspired optimization methods, Vorlesung SS 2021

07: Nature-inspired optimization methods, Vorlesung SS 2021

Pradyumn Shukla

„Vorsicht bei Corona-Angeboten im Internet“ - Die SECUSO Forschungsgruppe warnt vor Fakeshops - Campus-Report am 08.06.2021

„Vorsicht bei Corona-Angeboten im Internet“ - Die SECUSO Forschungsgruppe warnt vor Fakeshops - Campus-Report am 08.06.2021

Stefan Fuchs

05: Nature-inspired optimization methods, Vorlesung SS 2021, 21.05.2021

05: Nature-inspired optimization methods, Vorlesung SS 2021, 21.05.2021

Pradyumn Shukla

06: Nature-inspired optimization methods, Vorlesung SS 2021, 21.05.2021

06: Nature-inspired optimization methods, Vorlesung SS 2021, 21.05.2021

Pradyumn Shukla

04: Nature-inspired optimization methods. Vorlesung SS 2021, 20.05.2021

04: Nature-inspired optimization methods. Vorlesung SS 2021, 20.05.2021

Pradyumn Shukla

03: Nature-inspired optimization methods, Vorlesung SS 2021, 19.05.2021

03: Nature-inspired optimization methods, Vorlesung SS 2021, 19.05.2021

Pradyumn Shukla

02: Nature-inspired optimization methods, Vorlesung SS 2021, 18.05.2021

02: Nature-inspired optimization methods, Vorlesung SS 2021, 18.05.2021

Pradyumn Shukla

01: Nature-inspired optimization methods, Vorlesung SS 2021, 17.05.2021

01: Nature-inspired optimization methods, Vorlesung SS 2021, 17.05.2021

Pradyumn Shukla

11: Search operators, Vorlesung, WS 2020/21, 08.02.2021

11: Search operators, Vorlesung, WS 2020/21, 08.02.2021

Pradyumn Shukla

reFuels – Kraftstoffe neu denken – mit englischem Untertiteln

reFuels – Kraftstoffe neu denken – mit englischem Untertiteln

Sandra Wiebe

„Möglichst gratis und ohne lange Wege“ - Die KIT-Verhaltensökonomin Nora Szech zur Bereitschaft der Menschen, sich einem Covid-Test zu unterziehen - Campusreport am 19.01.2021

„Möglichst gratis und ohne lange Wege“ - Die KIT-Verhaltensökonomin Nora Szech zur Bereitschaft der Menschen, sich einem Covid-Test zu unterziehen - Campusreport am 19.01.2021

Stefan Fuchs

10: Application 1: Fairness Modelling in Allocation Problems, Vorlesung, WS 2020/21, 07.01.2021

10: Application 1: Fairness Modelling in Allocation Problems, Vorlesung, WS 2020/21, 07.01.2021

Pradyumn Shukla

9: Robust Optimization, Vorlesung, WS 2020/21, 23.12.2020

9: Robust Optimization, Vorlesung, WS 2020/21, 23.12.2020

Pradyumn Shukla

7: Understanding how GAs work. Vorlesung WS, 2020/21, 22.12.2020

7: Understanding how GAs work. Vorlesung WS, 2020/21, 22.12.2020

Pradyumn Shukla

4: Evolutionary Algorithms: Inspiration, Vorlesung, WS 2020/21, 17.12.2020

4: Evolutionary Algorithms: Inspiration, Vorlesung, WS 2020/21, 17.12.2020

Pradyumn Shukla

2: Statistical Foundations: Monte Carlo and Quasi Monte Carlo Methods, Vorlesung, WS 2020/21, 15.12.2020

2: Statistical Foundations: Monte Carlo and Quasi Monte Carlo Methods, Vorlesung, WS 2020/21, 15.12.2020

Pradyumn Shukla

8: Multi-objective Algorithms, Vorlesung, WS 2020/21, 23.12.2020

8: Multi-objective Algorithms, Vorlesung, WS 2020/21, 23.12.2020

Pradyumn Shukla

6: Evolutionary Algorithms: Selective Pressure, Vorlesung, WS 2020/21, 21.12.2020

6: Evolutionary Algorithms: Selective Pressure, Vorlesung, WS 2020/21, 21.12.2020

Pradyumn Shukla

„Die Vermessung der Unsicherheit“ - Am Karlsruher Institut für Technologie werden Prognosen zum Verlauf der Covid-Epidemie optimiert - Campus-Report am 29.12.2020

„Die Vermessung der Unsicherheit“ - Am Karlsruher Institut für Technologie werden Prognosen zum Verlauf der Covid-Epidemie optimiert - Campus-Report am 29.12.2020

Stefan Fuchs

5: Evolutionary Algorithms: Selection. Vorlesung, WS 2020/21, 18.12.2020

5: Evolutionary Algorithms: Selection. Vorlesung, WS 2020/21, 18.12.2020

Pradyumn Shukla

3: Statistical Foundations: Analysis of Computer Experiments, Vorlesung, WS 2020/21, 16.12.2020

3: Statistical Foundations: Analysis of Computer Experiments, Vorlesung, WS 2020/21, 16.12.2020

Pradyumn Shukla

1: Statistical Foundations: Random Number Generation, Vorlesung, WS 2020/21, 14.12.2020

1: Statistical Foundations: Random Number Generation, Vorlesung, WS 2020/21, 14.12.2020

Pradyumn Shukla