Index Catalog // Arch : Northwestern University Institutional Repository

Description:: Robust optimization is a distinct approach to optimizations problems that allows for the incorporation of uncertainty. The usefulness of robust optimization lies in the ability to solve for every realization of the uncertain parameters. As a result, the problem can be solved for the worst-case scenarios of the entire set...
Keyword:: Data Driven
Creator:: Watson Fu
Owner:: Scholarly Digital Publishing
Date Uploaded:: 11/30/2018
Date Modified:: 12/04/2018

Description:: The chance-constrained method is one of the major approaches to solving optimization problems under various uncertainties. It is a formulation of an optimization problem that ensures that the probability of meeting a certain constraint is above a certain level. In other words, it restricts the feasible region so that the...
Keyword:: Optimization and Chance-constraint method
Creator:: Cristine Li
Owner:: Scholarly Digital Publishing
Date Uploaded:: 11/30/2018
Date Modified:: 11/30/2018

Description:: Quadratic programming (QP) is the problem of optimizing a quadratic objective function and is one of the simplests form of non-linear programming. The objective function can contain bilinear or up to second order polynomial terms, and the constraints are linear and can be both equalities and inequalities. QP is widely...
Keyword:: Quadratic Programming
Creator:: Jack Heider
Owner:: Scholarly Digital Publishing
Date Uploaded:: 11/30/2018
Date Modified:: 11/30/2018

Description:: Sigmoid problems are a class of optimization problems with the objective of maximizing the sum of multiple sigmoid functions. They are defined by their limits at negative and positive infinity. Similar to the unit step function the function approaches 1 as it approaches infinity and approaches -1 as it approaches...
Keyword:: Signomial
Creator:: Matthew Hantzmon
Owner:: Scholarly Digital Publishing
Date Uploaded:: 11/30/2018
Date Modified:: 11/30/2018

Description:: Column generation algorithms are used for MILP problems. The formulation was initially proposed by Ford and Fulkerson in 1958 . The main advantage of column generation is that not all possibilities need to be enumerated. Instead, the problem is first formulated as a restricted master problem (RMP). This RMP has...
Keyword:: Column Generations and Algorithms
Creator:: Kedric Daly
Owner:: Scholarly Digital Publishing
Date Uploaded:: 11/29/2018
Date Modified:: 11/29/2018

Description:: Optimization with absolute values is a special case of linear programming in which a problem made nonlinear due to the presence of absolute values is solved using linear programming methods. Absolute value functions themselves are very difficult to perform standard optimization procedures on. They are not continuously differentiable functions, are...
Keyword:: Absolute Values and Optimization
Creator:: Benjamin Granger, Kathleen Zhou, and Marta Yu
Owner:: Scholarly Digital Publishing
Date Uploaded:: 11/29/2018
Date Modified:: 11/29/2018

Description:: Computational complexity refers to the amount of resources required to solve a type of problem by systematic application of an algorithm. Resources that can be considered include the amount of communications, gates in a circuit, or the number of processors. Because the size of the particular input to a problem...
Keyword:: Computational and Complexity
Creator:: David O'Brien, Mark Caswell, and David Chen
Owner:: Scholarly Digital Publishing
Date Uploaded:: 11/29/2018
Date Modified:: 11/29/2018

LIBRARIES | ARCH