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Optimisation Problems: Methods and Constraints
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Related lectures (39)
Optimization Methods: Theory Discussion
Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.
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Optimisation in Energy Systems
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
Linear Programming: Weighted Bipartite Matching
Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.
Optimization with Constraints: KKT Conditions
Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.
Energy System Modeling: Optimization and Performance Indicators
Explores energy system modeling using optimization techniques and performance indicators.
Constraints and Lagrange
Introduces constraints, Lagrange multipliers, and generalized coordinates in physics.
Linear Optimization Techniques: Problem Solving Approaches
Provides an overview of linear optimization techniques, focusing on problem-solving methods and the importance of constraints and objective functions.
Optimization with Constraints: KKT Conditions
Covers the optimization with constraints, focusing on the Karush-Kuhn-Tucker (KKT) conditions.
Optimization in Energy Systems
Explores optimization in energy systems, focusing on decision-making through objective functions and constraints to determine system states.
Thermodynamic Properties: Equations and Models
Explains thermodynamic properties, equations of state, and mixture rules for energy systems modeling.
Discrete Optimization: Relaxation
MOOC: Optimization: principles and algorithms - Network and discrete optimization
Explores solving discrete optimization problems by relaxing integrality constraints.
Discrete optimization: Knapsack
MOOC: Optimization: principles and algorithms - Network and discrete optimization
Explores modeling classic optimization problems as mixed integer linear problems, focusing on the knapsack problem and its applications.
Hedging for LPs
Covers the concept of hedging for Linear Programs and the simplex method, focusing on minimizing costs and finding optimal solutions.
Approximation Algorithms
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Optimization in Engineering
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Explores optimization methods in engineering, covering decision variables, constraints, and various solving techniques.
Cutset Formulation: MST Problem
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Explores the cutset formulation for the MST Problem and Gomory Cutting Planes method.
Integer Programs: Optimization and Constraints
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Explores integer programs, nonconvex optimization, constraints, and geometric aspects of linear programming for optimal solutions.
Linear Programming Basics
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Introduces linear programming basics, including optimization problems, cost functions, simplex algorithm, geometry of linear programs, extreme points, and degeneracy.
Optimization Problems: Path Finding and Portfolio Allocation
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Covers optimization problems in path finding and portfolio allocation.
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