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„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
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
„Nur ein digitales Standbein kann den städtischen Einzelhandel retten“ - Am Karlsruher Institut für Technologie untersucht man das veränderte Kaufverhalten durch Corona - Campus-Report am 18.05.2021
„Nur ein digitales Standbein kann den städtischen Einzelhandel retten“ - Am Karlsruher Institut für Technologie untersucht man das veränderte Kaufverhalten durch Corona - Campus-Report am 18.05.2021
Stefan Fuchs
„Alles außer beamen“ - Das am KIT entwickelte Mobilitätskonzept „Regiomove“ bietet grenzenlose Mobilität und schont das Klima - Campus-Report am 13.04.2021
„Alles außer beamen“ - Das am KIT entwickelte Mobilitätskonzept „Regiomove“ bietet grenzenlose Mobilität und schont das Klima - Campus-Report am 13.04.2021
Stefan Fuchs
„Mehr Auto, mehr Fahrrad, weniger Busse & Bahnen“ - Am KIT wird das Deutsche Mobilitätspanel für das Coronajahr 2020 ausgewertet" - Campus Report am 06.04.2021
„Mehr Auto, mehr Fahrrad, weniger Busse & Bahnen“ - Am KIT wird das Deutsche Mobilitätspanel für das Coronajahr 2020 ausgewertet" - Campus Report am 06.04.2021
Stefan Fuchs
„Mein Paket kommt mit der Straßenbahn“ - am KIT startete im März das Forschungsprojekt LogIKTram - Campus Report am 23.03.2021
„Mein Paket kommt mit der Straßenbahn“ - am KIT startete im März das Forschungsprojekt LogIKTram - Campus Report am 23.03.2021
Stefan Fuchs
11: Search operators, Vorlesung, WS 2020/21, 08.02.2021
11: Search operators, Vorlesung, WS 2020/21, 08.02.2021
Pradyumn Shukla
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
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