TY - JOUR
AB - The paper introduces an inertial extragradient subgradient method with self-adaptive step sizes for solving equilibrium problems in real Hilbert spaces. Weak convergence of the proposed method is obtained under the condition that the bifunction is pseudomonotone and Lipchitz continuous. Linear convergence is also given when the bifunction is strongly pseudomonotone and Lipchitz continuous. Numerical implementations and comparisons with other related inertial methods are given using test problems including a real-world application to Nash–Cournot oligopolistic electricity market equilibrium model.
AU - Shehu, Yekini
AU - Iyiola, Olaniyi S.
AU - Thong, Duong Viet
AU - Van, Nguyen Thi Cam
ID - 8817
IS - 2
JF - Mathematical Methods of Operations Research
SN - 14322994
TI - An inertial subgradient extragradient algorithm extended to pseudomonotone equilibrium problems
VL - 93
ER -
TY - CONF
AB - In the multiway cut problem we are given a weighted undirected graph G=(V,E) and a set T⊆V of k terminals. The goal is to find a minimum weight set of edges E′⊆E with the property that by removing E′ from G all the terminals become disconnected. In this paper we present a simple local search approximation algorithm for the multiway cut problem with approximation ratio 2−2k . We present an experimental evaluation of the performance of our local search algorithm and show that it greatly outperforms the isolation heuristic of Dalhaus et al. and it has similar performance as the much more complex algorithms of Calinescu et al., Sharma and Vondrak, and Buchbinder et al. which have the currently best known approximation ratios for this problem.
AU - Bloch-Hansen, Andrew
AU - Samei, Nasim
AU - Solis-Oba, Roberto
ID - 9227
SN - 0302-9743
T2 - Conference on Algorithms and Discrete Applied Mathematics
TI - Experimental evaluation of a local search approximation algorithm for the multiway cut problem
VL - 12601
ER -
TY - JOUR
AB - In this paper, we present two new inertial projection-type methods for solving multivalued variational inequality problems in finite-dimensional spaces. We establish the convergence of the sequence generated by these methods when the multivalued mapping associated with the problem is only required to be locally bounded without any monotonicity assumption. Furthermore, the inertial techniques that we employ in this paper are quite different from the ones used in most papers. Moreover, based on the weaker assumptions on the inertial factor in our methods, we derive several special cases of our methods. Finally, we present some experimental results to illustrate the profits that we gain by introducing the inertial extrapolation steps.
AU - Izuchukwu, Chinedu
AU - Shehu, Yekini
ID - 9234
IS - 2
JF - Networks and Spatial Economics
KW - Computer Networks and Communications
KW - Software
KW - Artificial Intelligence
SN - 1566-113X
TI - New inertial projection methods for solving multivalued variational inequality problems beyond monotonicity
VL - 21
ER -
TY - GEN
AB - Given a fixed finite metric space (V,μ), the {\em minimum 0-extension problem}, denoted as 0-Ext[μ], is equivalent to the following optimization problem: minimize function of the form minx∈Vn∑ifi(xi)+∑ijcijμ(xi,xj) where cij,cvi are given nonnegative costs and fi:V→R are functions given by fi(xi)=∑v∈Vcviμ(xi,v). The computational complexity of 0-Ext[μ] has been recently established by Karzanov and by Hirai: if metric μ is {\em orientable modular} then 0-Ext[μ] can be solved in polynomial time, otherwise 0-Ext[μ] is NP-hard. To prove the tractability part, Hirai developed a theory of discrete convex functions on orientable modular graphs generalizing several known classes of functions in discrete convex analysis, such as L♮-convex functions. We consider a more general version of the problem in which unary functions fi(xi) can additionally have terms of the form cuv;iμ(xi,{u,v}) for {u,v}∈F, where set F⊆(V2) is fixed. We extend the complexity classification above by providing an explicit condition on (μ,F) for the problem to be tractable. In order to prove the tractability part, we generalize Hirai's theory and define a larger class of discrete convex functions. It covers, in particular, another well-known class of functions, namely submodular functions on an integer lattice. Finally, we improve the complexity of Hirai's algorithm for solving 0-Ext on orientable modular graphs.
AU - Dvorak, Martin
AU - Kolmogorov, Vladimir
ID - 10045
KW - minimum 0-extension problem
KW - metric labeling problem
KW - discrete metric spaces
KW - metric extensions
KW - computational complexity
KW - valued constraint satisfaction problems
KW - discrete convex analysis
KW - L-convex functions
T2 - arXiv
TI - Generalized minimum 0-extension problem and discrete convexity
ER -
TY - CONF
AB - The Lovász Local Lemma (LLL) is a powerful tool in probabilistic combinatorics which can be used to establish the existence of objects that satisfy certain properties. The breakthrough paper of Moser and Tardos and follow-up works revealed that the LLL has intimate connections with a class of stochastic local search algorithms for finding such desirable objects. In particular, it can be seen as a sufficient condition for this type of algorithms to converge fast. Besides conditions for existence of and fast convergence to desirable objects, one may naturally ask further questions regarding properties of these algorithms. For instance, "are they parallelizable?", "how many solutions can they output?", "what is the expected "weight" of a solution?", etc. These questions and more have been answered for a class of LLL-inspired algorithms called commutative. In this paper we introduce a new, very natural and more general notion of commutativity (essentially matrix commutativity) which allows us to show a number of new refined properties of LLL-inspired local search algorithms with significantly simpler proofs.
AU - Harris, David G.
AU - Iliopoulos, Fotis
AU - Kolmogorov, Vladimir
ID - 10072
SN - 1868-8969
T2 - Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques
TI - A new notion of commutativity for the algorithmic Lovász Local Lemma
VL - 207
ER -
TY - CONF
AB - The convex grabbing game is a game where two players, Alice and Bob, alternate taking extremal points from the convex hull of a point set on the plane. Rational weights are given to the points. The goal of each player is to maximize the total weight over all points that they obtain. We restrict the setting to the case of binary weights. We show a construction of an arbitrarily large odd-sized point set that allows Bob to obtain almost 3/4 of the total weight. This construction answers a question asked by Matsumoto, Nakamigawa, and Sakuma in [Graphs and Combinatorics, 36/1 (2020)]. We also present an arbitrarily large even-sized point set where Bob can obtain the entirety of the total weight. Finally, we discuss conjectures about optimum moves in the convex grabbing game for both players in general.
AU - Dvorak, Martin
AU - Nicholson, Sara
ID - 9592
KW - convex grabbing game
KW - graph grabbing game
KW - combinatorial game
KW - convex geometry
T2 - Proceedings of the 33rd Canadian Conference on Computational Geometry
TI - Massively winning configurations in the convex grabbing game on the plane
ER -
TY - JOUR
AB - We consider inertial iteration methods for Fermat–Weber location problem and primal–dual three-operator splitting in real Hilbert spaces. To do these, we first obtain weak convergence analysis and nonasymptotic O(1/n) convergence rate of the inertial Krasnoselskii–Mann iteration for fixed point of nonexpansive operators in infinite dimensional real Hilbert spaces under some seemingly easy to implement conditions on the iterative parameters. One of our contributions is that the convergence analysis and rate of convergence results are obtained using conditions which appear not complicated and restrictive as assumed in other previous related results in the literature. We then show that Fermat–Weber location problem and primal–dual three-operator splitting are special cases of fixed point problem of nonexpansive mapping and consequently obtain the convergence analysis of inertial iteration methods for Fermat–Weber location problem and primal–dual three-operator splitting in real Hilbert spaces. Some numerical implementations are drawn from primal–dual three-operator splitting to support the theoretical analysis.
AU - Iyiola, Olaniyi S.
AU - Shehu, Yekini
ID - 9315
IS - 2
JF - Results in Mathematics
SN - 14226383
TI - New convergence results for inertial Krasnoselskii–Mann iterations in Hilbert spaces with applications
VL - 76
ER -
TY - JOUR
AB - In this paper, we consider reflected three-operator splitting methods for monotone inclusion problems in real Hilbert spaces. To do this, we first obtain weak convergence analysis and nonasymptotic O(1/n) convergence rate of the reflected Krasnosel'skiĭ-Mann iteration for finding a fixed point of nonexpansive mapping in real Hilbert spaces under some seemingly easy to implement conditions on the iterative parameters. We then apply our results to three-operator splitting for the monotone inclusion problem and consequently obtain the corresponding convergence analysis. Furthermore, we derive reflected primal-dual algorithms for highly structured monotone inclusion problems. Some numerical implementations are drawn from splitting methods to support the theoretical analysis.
AU - Iyiola, Olaniyi S.
AU - Enyi, Cyril D.
AU - Shehu, Yekini
ID - 9469
JF - Optimization Methods and Software
SN - 1055-6788
TI - Reflected three-operator splitting method for monotone inclusion problem
ER -
TY - JOUR
AB - In this paper, we propose a new iterative method with alternated inertial step for solving split common null point problem in real Hilbert spaces. We obtain weak convergence of the proposed iterative algorithm. Furthermore, we introduce the notion of bounded linear regularity property for the split common null point problem and obtain the linear convergence property for the new algorithm under some mild assumptions. Finally, we provide some numerical examples to demonstrate the performance and efficiency of the proposed method.
AU - Ogbuisi, Ferdinard U.
AU - Shehu, Yekini
AU - Yao, Jen Chih
ID - 9365
JF - Optimization
SN - 02331934
TI - Convergence analysis of new inertial method for the split common null point problem
ER -
TY - CONF
AB - We study a class of convex-concave saddle-point problems of the form minxmaxy⟨Kx,y⟩+fP(x)−h∗(y) where K is a linear operator, fP is the sum of a convex function f with a Lipschitz-continuous gradient and the indicator function of a bounded convex polytope P, and h∗ is a convex (possibly nonsmooth) function. Such problem arises, for example, as a Lagrangian relaxation of various discrete optimization problems. Our main assumptions are the existence of an efficient linear minimization oracle (lmo) for fP and an efficient proximal map for h∗ which motivate the solution via a blend of proximal primal-dual algorithms and Frank-Wolfe algorithms. In case h∗ is the indicator function of a linear constraint and function f is quadratic, we show a O(1/n2) convergence rate on the dual objective, requiring O(nlogn) calls of lmo. If the problem comes from the constrained optimization problem minx∈Rd{fP(x)|Ax−b=0} then we additionally get bound O(1/n2) both on the primal gap and on the infeasibility gap. In the most general case, we show a O(1/n) convergence rate of the primal-dual gap again requiring O(nlogn) calls of lmo. To the best of our knowledge, this improves on the known convergence rates for the considered class of saddle-point problems. We show applications to labeling problems frequently appearing in machine learning and computer vision.
AU - Kolmogorov, Vladimir
AU - Pock, Thomas
ID - 10552
T2 - 38th International Conference on Machine Learning
TI - One-sided Frank-Wolfe algorithms for saddle problems
ER -