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In computer science, program optimization, code optimization, or software optimization is the process of modifying a software system to make some aspect of it work more efficiently or use fewer resources.[1] In general, a computer program may be optimized so that it executes more rapidly, or to make it capable of operating with less memory storage or other resources, or draw less power.
^Robert Sedgewick, Algorithms, 1984, p. 84.
and 24 Related for: Program optimization information
In computer science, programoptimization, code optimization, or software optimization is the process of modifying a software system to make some aspect...
generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from...
compiler will then try to optimize the result. Whole programoptimization (WPO) is the compiler optimization of a program using information about all...
single-objective optimization cases are presented. In the second part, test functions with their respective Pareto fronts for multi-objective optimization problems...
Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets (or, equivalently...
optimizations could be performed. Because of these factors, optimization rarely produces "optimal" output in any sense, and in fact, an "optimization"...
In mathematics, nonlinear programming (NLP) is the process of solving an optimization problem where some of the constraints are not linear equalities...
OptimizationProgramming Language (OPL) is an algebraic modeling language for mathematical optimization models, which makes the coding easier and shorter...
Discrete optimization is a branch of optimization in applied mathematics and computer science. As opposed to continuous optimization, some or all of the...
An integer programming problem is a mathematical optimization or feasibility program in which some or all of the variables are restricted to be integers...
under Craig Chambers on compilers and whole-programoptimization techniques for object-oriented programming languages. He was elected to the National Academy...
Linear programming (LP), also called linear optimization, is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical...
"computer programming." To avoid confusion, some practitioners prefer the term "optimization" — e.g., "quadratic optimization." The quadratic programming problem...
science and economics, an optimization problem is the problem of finding the best solution from all feasible solutions. Optimization problems can be divided...
In mathematical optimization, constrained optimization (in some contexts called constraint optimization) is the process of optimizing an objective function...
liveness. Program analysis focuses on two major areas: programoptimization and program correctness. The first focuses on improving the program’s performance...
Proximal policy optimization (PPO) is an algorithm in the field of reinforcement learning that trains a computer agent's decision function to accomplish...
and A Basis for ProgramOptimization established intervals as the context for efficient and effective data flow analysis and optimization. Her 1971 paper...
In the optimization literature this relationship is called the Bellman equation. In terms of mathematical optimization, dynamic programming usually refers...
would alter the program's behavior, the most common being the return value optimization (see below). Another widely implemented optimization, described in...
Gurobi Optimizer is a prescriptive analytics platform and a decision-making technology developed by Gurobi Optimization, LLC. The Gurobi Optimizer (often...
several different types of programoptimization by specialization. The most straightforward application is to produce new programs that run faster than the...
each cut based on programmed feed rates, reducing cycling time. The optimization is said to reduce the amount of scrapped parts, broken tools, and cutter...
Bayesian optimization is a sequential design strategy for global optimization of black-box functions that does not assume any functional forms. It is usually...
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