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N2 - We study linear optimization problems over the cone of copositive matrices. These problems appear in nonconvex quadratic and binary optimization; for instance, the maximum clique problem and other combinatorial problems can be reformulated as such problems. We present new polyhedral inner and outer approximations of the copositive cone which we show to be exact in the limit.

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In contrast to previous approximation schemes, our approximation is not necessarily uniform for the whole cone but can be guided adaptively through the objective function, yielding a good approximation in those parts of the cone that are relevant for the optimization and only a coarse approximation in those parts that are not.

Using these approximations, we derive an adaptive linear approximation algorithm for copositive programs. However, some problems have distinct optimal solutions; for example, the problem of finding a feasible solution to a system of linear inequalities is a linear programming problem in which the objective function is the zero function that is, the constant function taking the value zero everywhere. For this feasibility problem with the zero-function for its objective-function, if there are two distinct solutions, then every convex combination of the solutions is a solution.

The vertices of the polytope are also called basic feasible solutions. The reason for this choice of name is as follows. Let d denote the number of variables. Thereby we can study these vertices by means of looking at certain subsets of the set of all constraints a discrete set , rather than the continuum of LP solutions. This principle underlies the simplex algorithm for solving linear programs. The simplex algorithm , developed by George Dantzig in , solves LP problems by constructing a feasible solution at a vertex of the polytope and then walking along a path on the edges of the polytope to vertices with non-decreasing values of the objective function until an optimum is reached for sure.

In many practical problems, " stalling " occurs: many pivots are made with no increase in the objective function. In practice, the simplex algorithm is quite efficient and can be guaranteed to find the global optimum if certain precautions against cycling are taken.

The simplex algorithm has been proved to solve "random" problems efficiently, i. However, the simplex algorithm has poor worst-case behavior: Klee and Minty constructed a family of linear programming problems for which the simplex method takes a number of steps exponential in the problem size. Like the simplex algorithm of Dantzig, the criss-cross algorithm is a basis-exchange algorithm that pivots between bases. However, the criss-cross algorithm need not maintain feasibility, but can pivot rather from a feasible basis to an infeasible basis.

The criss-cross algorithm does not have polynomial time-complexity for linear programming.

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In contrast to the simplex algorithm, which finds an optimal solution by traversing the edges between vertices on a polyhedral set, interior-point methods move through the interior of the feasible region. This is the first worst-case polynomial-time algorithm ever found for linear programming. To solve a problem which has n variables and can be encoded in L input bits, this algorithm uses O n 4 L pseudo-arithmetic operations on numbers with O L digits. Leonid Khachiyan solved this long-standing complexity issue in with the introduction of the ellipsoid method. The convergence analysis has real-number predecessors, notably the iterative methods developed by Naum Z.

Shor and the approximation algorithms by Arkadi Nemirovski and D. Khachiyan's algorithm was of landmark importance for establishing the polynomial-time solvability of linear programs. The algorithm was not a computational break-through, as the simplex method is more efficient for all but specially constructed families of linear programs.

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Linear Optimization and Approximation

However, Khachiyan's algorithm inspired new lines of research in linear programming. In , N. Karmarkar proposed a projective method for linear programming. Karmarkar's algorithm improved on Khachiyan's worst-case polynomial bound giving O n 3. Karmarkar claimed that his algorithm was much faster in practical LP than the simplex method, a claim that created great interest in interior-point methods.

Affine scaling is one of the oldest interior point methods to be developed. It was developed in the Soviet Union in the mids, but didn't receive much attention until the discovery of Karmarkar's algorithm, after which affine scaling was reinvented multiple times and presented as a simplified version of Karmarkar's. Affine scaling amounts to doing gradient descent steps within the feasible region, while rescaling the problem to make sure the steps move toward the optimum faster. In , Vaidya developed an algorithm that runs in O n 2.

For both theoretical and practical purposes, barrier function or path-following methods have been the most popular interior point methods since the s.

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The current opinion is that the efficiencies of good implementations of simplex-based methods and interior point methods are similar for routine applications of linear programming. Covering and packing LPs can be solved approximately in nearly-linear time. There are several open problems in the theory of linear programming, the solution of which would represent fundamental breakthroughs in mathematics and potentially major advances in our ability to solve large-scale linear programs.

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  8. This closely related set of problems has been cited by Stephen Smale as among the 18 greatest unsolved problems of the 21st century. In Smale's words, the third version of the problem "is the main unsolved problem of linear programming theory. The development of such algorithms would be of great theoretical interest, and perhaps allow practical gains in solving large LPs as well.

    Although the Hirsch conjecture was recently disproved for higher dimensions, it still leaves the following questions open.

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    These questions relate to the performance analysis and development of simplex-like methods. The immense efficiency of the simplex algorithm in practice despite its exponential-time theoretical performance hints that there may be variations of simplex that run in polynomial or even strongly polynomial time. It would be of great practical and theoretical significance to know whether any such variants exist, particularly as an approach to deciding if LP can be solved in strongly polynomial time. The simplex algorithm and its variants fall in the family of edge-following algorithms, so named because they solve linear programming problems by moving from vertex to vertex along edges of a polytope.

    This means that their theoretical performance is limited by the maximum number of edges between any two vertices on the LP polytope. As a result, we are interested in knowing the maximum graph-theoretical diameter of polytopal graphs. It has been proved that all polytopes have subexponential diameter. The recent disproof of the Hirsch conjecture is the first step to prove whether any polytope has superpolynomial diameter.

    If any such polytopes exist, then no edge-following variant can run in polynomial time. Questions about polytope diameter are of independent mathematical interest. Simplex pivot methods preserve primal or dual feasibility. On the other hand, criss-cross pivot methods do not preserve primal or dual feasibility—they may visit primal feasible, dual feasible or primal-and-dual infeasible bases in any order. Pivot methods of this type have been studied since the s. In contrast to polytopal graphs, graphs of arrangement polytopes are known to have small diameter, allowing the possibility of strongly polynomial-time criss-cross pivot algorithm without resolving questions about the diameter of general polytopes.

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    If all of the unknown variables are required to be integers, then the problem is called an integer programming IP or integer linear programming ILP problem. 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. This problem is also classified as NP-hard, and in fact the decision version was one of Karp's 21 NP-complete problems. If only some of the unknown variables are required to be integers, then the problem is called a mixed integer programming MIP problem.

    There are however some important subclasses of IP and MIP problems that are efficiently solvable, most notably problems where the constraint matrix is totally unimodular and the right-hand sides of the constraints are integers or — more general — where the system has the total dual integrality TDI property. Such integer-programming algorithms are discussed by Padberg and in Beasley. A linear program in real variables is said to be integral if it has at least one optimal solution which is integral.

    Integral linear programs are of central importance in the polyhedral aspect of combinatorial optimization since they provide an alternate characterization of a problem. Conversely, if we can prove that a linear programming relaxation is integral, then it is the desired description of the convex hull of feasible integral solutions. Terminology is not consistent throughout the literature, so one should be careful to distinguish the following two concepts,. One common way of proving that a polyhedron is integral is to show that it is totally unimodular.

    There are other general methods including the integer decomposition property and total dual integrality. A bounded integral polyhedron is sometimes called a convex lattice polytope , particularly in two dimensions. Permissive licenses:. MINTO Mixed Integer Optimizer, an integer programming solver which uses branch and bound algorithm has publicly available source code [26] but is not open source. Proprietary licenses:.

    Optimization Problems

    A reader may consider beginning with Nering and Tucker, with the first volume of Dantzig and Thapa, or with Williams. From Wikipedia, the free encyclopedia. Main article: Dual linear program. In a linear programming problem, a series of linear constraints produces a convex feasible region of possible values for those variables. In the two-variable case this region is in the shape of a convex simple polygon. Main article: Karmarkar's algorithm. Main article: Affine scaling. Does linear programming admit a strongly polynomial-time algorithm?

    The KKT conditions play no role in the convergence analysis of the approximating scheme we propose in this work. However, it is crucial in the process of obtaining analytical optimal solutions to compare the numerical experiments with. On the other hand, the convergence set up provides the means from which it is possible to establish the KKT optimality conditions. Let us now state and prove them.