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58
Randomized Simplex Algorithms on KleeMinty Cubes
 COMBINATORICA
, 1994
"... We investigate the behavior of randomized simplex algorithms on special linear programs. For this, we use combinatorial models for the KleeMinty cubes [22] and similar linear programs with exponential decreasing paths. The analysis of two most natural randomized pivot rules on the KleeMinty cubes ..."
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Cited by 21 (6 self)
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We investigate the behavior of randomized simplex algorithms on special linear programs. For this, we use combinatorial models for the KleeMinty cubes [22] and similar linear programs with exponential decreasing paths. The analysis of two most natural randomized pivot rules on the KleeMinty cubes leads to (nearly) quadratic lower bounds for the complexity of linear programming with random pivots. Thus we disprove two bounds (for the expected running time of the randomedge simplex algorithm on KleeMinty cubes) conjectured in the literature. At the same time, we establish quadratic upper bounds for the expected length of a path for a simplex algorithm with random pivots on the classes of linear programs under investigation. In contrast to this, we find that the average length of an increasing path in a KleeMinty cube is exponential when all paths are taken with equal probability.
An exponential lower bound on the complexity of regularization paths
, 2009
"... For a variety of regularization methods, algorithms computing the entire solution path have been developed recently. Solution path algorithms do not only compute the solution for one particular value of the regularization parameter but the entire path of solutions, making the selection of an optimal ..."
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Cited by 16 (4 self)
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For a variety of regularization methods, algorithms computing the entire solution path have been developed recently. Solution path algorithms do not only compute the solution for one particular value of the regularization parameter but the entire path of solutions, making the selection of an optimal parameter much easier. It has been assumed that these piecewise linear solution paths have only linear complexity, i.e. linearly many bends. We prove that for the support vector machine this complexity can indeed be exponential in the number of training points in the worst case.
SOME PROPERTIES OF THE GAMMA AND PSI FUNCTIONS, WITH APPLICATIONS
"... Abstract. In this paper, some monotoneity and concavity properties of the gamma, beta and psi functions are obtained, from which several asymptotically sharp inequalities follow. Applying these properties, the authors improve some wellknown results for the volume Ωn of the unit ball B n ⊂ R n,thesu ..."
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Cited by 15 (0 self)
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Abstract. In this paper, some monotoneity and concavity properties of the gamma, beta and psi functions are obtained, from which several asymptotically sharp inequalities follow. Applying these properties, the authors improve some wellknown results for the volume Ωn of the unit ball B n ⊂ R n,thesurface area ωn−1 of the unit sphere S n−1, and some related constants. 1.
On Tail Decay and Moment Estimates of a Condition Number for Random Linear Conic Systems
, 2003
"... In this paper we study the distribution tails and the moments of C (A) and log C (A), where C (A) is a condition number for the linear conic system Ax 0, x 6= 0, with A 2 IR . We consider the case where A is a Gaussian random matrix. For this input model we characterise the exact decay rates of ..."
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Cited by 13 (8 self)
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In this paper we study the distribution tails and the moments of C (A) and log C (A), where C (A) is a condition number for the linear conic system Ax 0, x 6= 0, with A 2 IR . We consider the case where A is a Gaussian random matrix. For this input model we characterise the exact decay rates of the distribution tails, we improve the existing moment estimates, and we prove various limit theorems for the cases where either n or m and n tend to in nity. Our results are of complexity theoretic interest, because interiorpoint methods and relaxation methods for the solution of Ax 0, x 6= 0 have running times that are bounded in terms of log C (A) and C (A) respectively. AMS Classi cation: primary 90C31,15A52; secondary 90C05,90C60,62H10. Key Words: condition number, random matrices, linear programming, probabilistic analysis, complexity theory.
A Survey on Pivot Rules for Linear Programming
 ANNALS OF OPERATIONS RESEARCH. (SUBMITTED
, 1991
"... The purpose of this paper is to survey the various pivot rules of the simplex method or its variants that have been developed in the last two decades, starting from the appearance of the minimal index rule of Bland. We are mainly concerned with the finiteness property of simplex type pivot rules. Th ..."
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Cited by 11 (1 self)
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The purpose of this paper is to survey the various pivot rules of the simplex method or its variants that have been developed in the last two decades, starting from the appearance of the minimal index rule of Bland. We are mainly concerned with the finiteness property of simplex type pivot rules. There are some other important topics in linear programming, e.g. complexity theory or implementations, that are not included in the scope of this paper. We do not discuss ellipsoid methods nor interior point methods. Well known classical results concerning the simplex method are also not particularly discussed in this survey, but the connection between the new methods and the classical ones are discussed if there is any. In this paper we discuss three classes of recently developed pivot rules for linear programming. The first class (the largest one) of the pivot rules we discuss is the class of essentially combinatorial pivot rules. Namely these rules only use labeling and signs of the variab...
Greedy Basis Pursuit
, 2006
"... We introduce Greedy Basis Pursuit (GBP), a new algorithm for computing signal representations using overcomplete dictionaries. GBP is rooted in computational geometry and exploits an equivalence between minimizing the ℓ 1norm of the representation coefficients and determining the intersection of th ..."
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We introduce Greedy Basis Pursuit (GBP), a new algorithm for computing signal representations using overcomplete dictionaries. GBP is rooted in computational geometry and exploits an equivalence between minimizing the ℓ 1norm of the representation coefficients and determining the intersection of the signal with the convex hull of the dictionary. GBP unifies the different advantages of previous algorithms: like standard approaches to Basis Pursuit, GBP computes representations that have minimum ℓ 1norm; like greedy algorithms such as Matching Pursuit, GBP builds up representations, sequentially selecting atoms. We describe the algorithm, demonstrate its performance, and provide code. Experiments show that GBP can provide a fast alternative to standard linear programming approaches to Basis Pursuit.
An FPGA implementation of the simplex algorithm
, 2006
"... Linear programming is applied to a large variety of scientific computing applications and industrial optimization problems. The Simplex algorithm is widely used for solving linear programs due to its robustness and scalability properties. However, application of the current software implementations ..."
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Cited by 9 (2 self)
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Linear programming is applied to a large variety of scientific computing applications and industrial optimization problems. The Simplex algorithm is widely used for solving linear programs due to its robustness and scalability properties. However, application of the current software implementations of the Simplex algorithm to reallife optimization problems are time consuming when used as the bounding engine within an integer linear programming framework. This work aims to accelerate the Simplex algorithm by proposing a novel parameterizable hardware implementation of the algorithm on an FPGA. Evaluation of the proposed design using real problems demonstrates a speedup of up to 20 times over a highly optimized commercial software implementation running on a 3.4GHz Pentium 4 processor, which is itself 100 times faster than one of the main public domain solvers.
Probabilistic Analysis of Algorithms
 Probabilistic Methods for Algorithmic Discrete Mathematics, Algorithms and Combinatorics 16
, 1998
"... this paper. Of course, the first question we must answer is: what do we mean by a typical instance of a given size? Sometimes, there is a natural answer to this question. For example, in developing an algorithm which is typically efficent for an NPcomplete optimization problems on graphs, we might ..."
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Cited by 7 (0 self)
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this paper. Of course, the first question we must answer is: what do we mean by a typical instance of a given size? Sometimes, there is a natural answer to this question. For example, in developing an algorithm which is typically efficent for an NPcomplete optimization problems on graphs, we might assume that an n vertex input is equally likely to be any of the 2 2 ) labelled graphs with n vertices. This allows us to exploit any property which holds on almost all such graphs when developing the algorithm
Combinatorics with a geometric flavor: some examples
 in Visions in Mathematics Toward 2000 (Geometric and Functional Analysis, Special Volume
, 2000
"... In this paper I try to present my field, combinatorics, via five examples of combinatorial studies which have some geometric flavor. The first topic is Tverberg's theorem, a gem in combinatorial geometry, and various of its combinatorial and topological extensions. McMullen's upper bound t ..."
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Cited by 7 (0 self)
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In this paper I try to present my field, combinatorics, via five examples of combinatorial studies which have some geometric flavor. The first topic is Tverberg's theorem, a gem in combinatorial geometry, and various of its combinatorial and topological extensions. McMullen's upper bound theorem for the face numbers of convex polytopes and its many extensions is the second topic. Next are general properties of subsets of the vertices of the discrete ndimensional cube and some relations with questions of extremal and probabilistic combinatorics. Our fourth topic is tree enumeration and random spanning trees, and finally, some combinatorial and geometrical aspects of the simplex method for linear programming are considered.