Optimization algorithms

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Related lectures (55)

Optimization Methods MOOC: Optimization: principles and algorithms - Network and discrete optimization MOOC: Optimization: principles and algorithms - Unconstrained nonlinear optimization MOOC: Optimization: principles and algorithms - Linear optimization

Covers the Newton's local method in Python using NumPy for optimization.

Variance Reduction: Strategies and Applications Log in to Mediaspace to watch this video

Discusses variance reduction techniques in stochastic simulation, focusing on allocation strategies and replica generation algorithms.

Distributed Computing Execution Models Log in to Mediaspace to watch this video

Explores challenges in minimizing job completion time in distributed computing, focusing on data skew impact and efficient processing.

Linear Programming: Solving LPs Log in to Mediaspace to watch this video

Covers the process of solving Linear Programs (LPs) using the simplex method.

Dataflow Analysis: Optimization Log in to Mediaspace to watch this video

Explores dataflow analysis for optimization, including equations solving, live variables, reaching definitions, and very busy expressions.

Distributed Intelligent Systems: General Logistics and Course Overview Log in to Mediaspace to watch this video

Covers the general logistics, course rationale, prerequisites, organization, credits, workload, grading, and course content, including swarm intelligence, foraging strategies, and collective phenomena.

Dynamic Programming: How Many Ways to Make Change Log in to Mediaspace to watch this video

Demonstrates dynamic programming to find the number of ways to make change using different coin denominations.

Proximal Gradient Descent: Optimization Techniques in Machine Learning Log in to Mediaspace to watch this video

Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.

Introduction to Optimization Log in to Mediaspace to watch this video

Introduces linear algebra, calculus, and optimization basics in Euclidean spaces, emphasizing the power of optimization as a modeling tool.

Approximate Convergence and Optimization Log in to Mediaspace to watch this video

Explores approximate convergence, optimization theorems, and mirror descent in mathematical algorithms.

Single Inequality or Equality Constraint Log in to Mediaspace to watch this video

Covers single inequality or equality constraints and necessary optimality conditions in optimization problems.

Optimization Principles Log in to Mediaspace to watch this video

Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.

Faster Gradient Descent: Projected Optimization Techniques Log in to Mediaspace to watch this video

Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.

Semi-Definite Programming Log in to Mediaspace to watch this video

Covers semi-definite programming and optimization over positive semidefinite cones.

Dynamic Programming: Bellman-Ford and Dijkstra Log in to Mediaspace to watch this video

Explores dynamic programming with Bellman-Ford, Dijkstra, greedy strategies, and activity scheduling problems.

Dynamic Programming: Steinitz Sequence Log in to Mediaspace to watch this video

Explores dynamic programming with the Steinitz sequence to optimize solutions efficiently.

Dynamic Programming: Knapsack Log in to Mediaspace to watch this video

Explores dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.

Initial BFS: Finding Solutions Log in to Mediaspace to watch this video

Covers the concept of finding an initial BFS and solving related optimization problems.

Extreme Values and Optimization Log in to Mediaspace to watch this video

Covers extreme values, optimization conditions, feasible sets, and partition formation for optimization.

Linear Programming Basics Log in to Mediaspace to watch this video

Covers deriving basic linear program representation, finding solutions, and exploring optimality.

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