generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from...

convex optimization problems admit polynomial-time algorithms, whereas mathematical optimization is in general NP-hard. A convex optimization problem...

Robust optimization is a field of mathematical optimization theory that deals with optimization problems in which a certain measure of robustness is sought...

Combinatorial optimization is a subfield of mathematical optimization that consists of finding an optimal object from a finite set of objects, where the...

In mathematical optimization theory, duality or the duality principle is the principle that optimization problems may be viewed from either of two perspectives...

transformation between input and output values, described by a mathematical function, optimization deals with generating and selecting the best solution from...

Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute...

(often referred to as simply, “Gurobi”) is a solver, since it uses mathematical optimization to calculate the answer to a problem. Gurobi is included in the...

Continuous optimization is a branch of optimization in applied mathematics. As opposed to discrete optimization, the variables used in the objective function...

researchers active in optimization. The MOS encourages the research, development, and use of optimization—including mathematical theory, software implementation...

Bilevel optimization is a special kind of optimization where one problem is embedded (nested) within another. The outer optimization task is commonly referred...

hyperparameter optimization methods. Bayesian optimization is a global optimization method for noisy black-box functions. Applied to hyperparameter optimization, Bayesian...

Topology optimization is a mathematical method that optimizes material layout within a given design space, for a given set of loads, boundary conditions...

In mathematical optimization, constrained optimization (in some contexts called constraint optimization) is the process of optimizing an objective function...

Mathematical finance, also known as quantitative finance and financial mathematics, is a field of applied mathematics, concerned with mathematical modeling...

Derivative-free optimization (sometimes referred to as blackbox optimization) is a discipline in mathematical optimization that does not use derivative...

must be estimated for each technology. In mathematics, mathematical optimization (or optimization or mathematical programming) refers to the selection of...

when the function is at most linear. Linear algebra Mathematical optimization Convex optimization Linear programming Quadratic programming Scientific...

In mathematics, nonlinear programming (NLP) is the process of solving an optimization problem where some of the constraints are not linear equalities or...

Topological optimization techniques can then help work around the limitations of pure shape optimization. Mathematically, shape optimization can be posed...

Discrete optimization is a branch of optimization in applied mathematics and computer science. As opposed to continuous optimization, some or all of the...

process of solving certain mathematical optimization problems involving quadratic functions. Specifically, one seeks to optimize (minimize or maximize) a...

Dynamic programming is both a mathematical optimization method and an algorithmic paradigm. The method was developed by Richard Bellman in the 1950s and...

Simulation-based optimization (also known as simply simulation optimization) integrates optimization techniques into simulation modeling and analysis...

have applications in mathematical analysis, in mathematical optimization, and in game theory and economics. In nonlinear optimization, quasiconvex programming...

has several patents awarded. He has worked machine learning and mathematical optimization, and more recently on control theory and reinforcement learning...

developing a mathematical model is termed mathematical modeling. Mathematical models are used in applied mathematics and in the natural sciences (such as physics...

An integer programming problem is a mathematical optimization or feasibility program in which some or all of the variables are restricted to be integers...

In numerical analysis, hill climbing is a mathematical optimization technique which belongs to the family of local search. It is an iterative algorithm...

is a necessary condition for optimality associated with the mathematical optimization method known as dynamic programming. It writes the "value" of...