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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 many settings the term refers to integer linear programming (ILP), in which the objective function and the constraints (other than the integer constraints) are linear .
An integer value is typically specified in the source code of a program as a sequence of digits optionally prefixed with + or −. Some programming languages allow other notations, such as hexadecimal (base 16) or octal (base 8). Some programming languages also permit digit group separators. [2]
byte, short, int, long, char (integer types with a variety of ranges) float and double, floating-point numbers with single and double precisions; boolean, a Boolean type with logical values true and false; returnAddress, a value referring to an executable memory address. This is not accessible from the Java programming language and is usually ...
Fastest integer types that are guaranteed to be the fastest integer type available in the implementation, that has at least specified number n of bits. Guaranteed to be specified for at least N=8,16,32,64. Pointer integer types that are guaranteed to be able to hold a pointer. Included only if it is available in the implementation.
Cutting planes were proposed by Ralph Gomory in the 1950s as a method for solving integer programming and mixed-integer programming problems. However, most experts, including Gomory himself, considered them to be impractical due to numerical instability, as well as ineffective because many rounds of cuts were needed to make progress towards the solution.
In contrast to linear programming, which can be solved efficiently in the worst case, integer programming problems are in many practical situations (those with bounded variables) NP-hard. 0–1 integer programming or binary integer programming (BIP) is the special case of integer programming where variables are required to be 0 or 1 (rather ...
Several stochastic programming methods have been developed: Scenario-based methods including Sample Average Approximation; Stochastic integer programming for problems in which some variables must be integers; Chance constrained programming for dealing with constraints that must be satisfied with a given probability; Stochastic dynamic programming
In these languages, conversion refers to either implicitly or explicitly changing a value from one data type storage format to another, e.g. a 16-bit integer to a 32-bit integer. The storage needs may change as a result of the conversion, including a possible loss of precision or truncation.