By Stefania Bellavia, Gianmarco Gurioli, Benedetta Morini, and Philippe L. TointSIAM Journal on Optimization, Volume 29, Issue 4, Page 2881-2915, January 2019. A regularization algorithm using inexact function values and inexact derivatives is proposed and its evaluation complexity analyzed....
By Seyedehsomayeh Hosseini and André UschmajewSIAM Journal on Optimization, Volume 29, Issue 4, Page 2853-2880, January 2019. In this paper, a nonsmooth optimization method for locally Lipschitz functions on real algebraic varieties is developed....
By Ion Necoara, Peter Richtárik, and Andrei PatrascuSIAM Journal on Optimization, Volume 29, Issue 4, Page 2814-2852, January 2019. In this paper we develop a family of randomized projection methods (RPM) for solving the convex feasibility...
By Leslie Greengard and Shidong JiangSIAM Review, Volume 61, Issue 4, Page 733-755, January 2019. We present a new integral representation for the unsteady, incompressible Stokes or Navier--Stokes equations, based on a linear combination...
By Jon Cockayne, Chris J. Oates, T. J. Sullivan, and Mark GirolamiSIAM Review, Volume 61, Issue 4, Page 756-789, January 2019. Over forty years ago average-case error was proposed in the applied mathematics literature as an alternative criterion with which...
By Gábor PatakiSIAM Review, Volume 61, Issue 4, Page 839-859, January 2019. Semidefinite programs (SDPs)---some of the most useful and versatile optimization problems of the last few decades---are often...
By Catherine F. Higham and Desmond J. HighamSIAM Review, Volume 61, Issue 4, Page 860-891, January 2019. Multilayered artificial neural networks are becoming a pervasive tool in a host of application fields. At the heart of...
By Guo Luo and Thomas Y. HouSIAM Review, Volume 61, Issue 4, Page 793-835, January 2019. Whether the three-dimensional incompressible Euler equations can develop a singularity in finite time from smooth initial data is one...
By Olga Y. KushelSIAM Review, Volume 61, Issue 4, Page 643-729, January 2019. Multiplicative and additive $D$-stability, diagonal stability, Schur $D$-stability, and $H$-stability are classical concepts which arise in studying linear dynamical...
By Darinka DentchevaSIAM Review, Volume 61, Issue 4, Page 837-838, January 2019. The Education section of SIAM Review presents two papers in this issue. In the first paper Gábor Pataki ...
By Misha E. KilmerSIAM Review, Volume 61, Issue 4, Page 731-731, January 2019. Numerical solution of the Navier--Stokes equations in both two and three dimensions is the subject of the first of...
By Volker H. SchulzSIAM Review, Volume 61, Issue 4, Page 895-906, January 2019. The book reviews section serves to inform readers about current important books from a personal point of view. However,...
By The EditorsSIAM Review, Volume 61, Issue 4, Page 791-791, January 2019. The SIGEST article in this issue is “Formation of Finite-Time Singularities in the 3D Axisymmetric Euler Equations: A Numerics...
By J. M. Sanz-SernaSIAM Review, Volume 61, Issue 4, Page 641-641, January 2019. It is a safe bet that most readers use matrices in their work. It is often of interest to...
By Guanghui Lan and Yu YangSIAM Journal on Optimization, Volume 29, Issue 4, Page 2753-2784, January 2019. In this paper, we present new stochastic methods for solving two important classes of nonconvex optimization problems....
By Ying Cui, Defeng Sun, and Kim-Chuan TohSIAM Journal on Optimization, Volume 29, Issue 4, Page 2785-2813, January 2019. This paper introduces an efficient algorithm for computing the best approximation of a given matrix...
By Zhaosong Lu and Zirui ZhouSIAM Journal on Optimization, Volume 29, Issue 4, Page 2725-2752, January 2019. In this paper we consider a class of structured nonsmooth difference-of-convex (DC) minimization in which the first...
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By Minh N. Dao and Hung M. PhanSIAM Journal on Optimization, Volume 29, Issue 4, Page 2697-2724, January 2019. The Douglas--Rachford algorithm is a classical and powerful splitting method for minimizing the sum of two convex...
By Kuang Bai, Jane J. Ye, and Jin ZhangSIAM Journal on Optimization, Volume 29, Issue 4, Page 2625-2649, January 2019. In this paper we study sufficient conditions for metric subregularity of a set-valued map which is the...
By César Gutiérrez, Lidia Huerga, Vicente Novo, and Miguel SamaSIAM Journal on Optimization, Volume 29, Issue 4, Page 2677-2696, January 2019. In the framework of a vector optimization problem, we provide conditions for approximate proper solutions to tend...
By Marcel K. de Carli Silva and Levent TunçelSIAM Journal on Optimization, Volume 29, Issue 4, Page 2650-2676, January 2019. The MaxCut approximation algorithm by Goemans and Williamson is one of the most celebrated results in semidefinite...
By Paul Breiding, Khazhgali Kozhasov, and Antonio LerarioSIAM Journal on Optimization, Volume 29, Issue 4, Page 2608-2624, January 2019. Spectrahedra are affine-linear sections of the cone $\mathcal{P}_n$ of positive semidefinite symmetric $n\times n$-matrices. We consider random...
By Biel Roig-Solvas, Lee Makowski, and Dana H. BrooksSIAM Journal on Optimization, Volume 29, Issue 4, Page 2594-2607, January 2019. Proximal algorithms have gained popularity in recent years in large-scale and distributed optimization problems. One such problem...
By Weiwei Kong, Jefferson G. Melo, and Renato D. C. MonteiroSIAM Journal on Optimization, Volume 29, Issue 4, Page 2566-2593, January 2019. This paper analyzes the iteration complexity of a quadratic penalty accelerated inexact proximal point method for solving...
By M. Gürbüzbalaban, A. Ozdaglar, and P. A. ParriloSIAM Journal on Optimization, Volume 29, Issue 4, Page 2542-2565, January 2019. The incremental gradient (IG) method is a prominent algorithm for minimizing a finite sum of smooth convex...
By O. P. Ferreira, M. S. Louzeiro, and L. F. PrudenteSIAM Journal on Optimization, Volume 29, Issue 4, Page 2517-2541, January 2019. The gradient method for minimizing a differentiable convex function on Riemannian manifolds with lower bounded sectional curvature...
By Peter Ochs and Thomas PockSIAM Journal on Optimization, Volume 29, Issue 4, Page 2482-2503, January 2019. In this paper we propose an adaptively extrapolated proximal gradient method, which is based on the accelerated...
By Ronny Bergmann and Roland HerzogSIAM Journal on Optimization, Volume 29, Issue 4, Page 2423-2444, January 2019. Karush--Kuhn--Tucker (KKT) conditions for equality and inequality constrained optimization problems on smooth manifolds are formulated. Under the...
By E. Bednarczuk and K. RutkowskiSIAM Journal on Optimization, Volume 29, Issue 4, Page 2504-2516, January 2019. We give sufficient conditions for Lipschitz-likeness of a class of polyhedral set-valued mappings in Hilbert spaces based...
By Stephen Becker, Jalal Fadili, and Peter OchsSIAM Journal on Optimization, Volume 29, Issue 4, Page 2445-2481, January 2019. We introduce a framework for quasi-Newton forward-backward splitting algorithms (proximal quasi-Newton methods) with a metric induced by...
By Jinhua Wang, Yaohua Hu, Carisa Kwok Wai Yu, Chong Li, and Xiaoqi YangSIAM Journal on Optimization, Volume 29, Issue 3, Page 2388-2421, January 2019. We consider the extended Newton method for approaching a Pareto optimum of a multiobjective optimization problem, ...
By Zongshan Shen, Jen-Chih Yao, and Xi Yin ZhengSIAM Journal on Optimization, Volume 29, Issue 3, Page 2291-2319, January 2019. In contrast to many existing results on calmness (metric subregularity) of multifunctions established by using dual notions...
By Zhengling Qi, Ying Cui, Yufeng Liu, and Jong-Shi PangSIAM Journal on Optimization, Volume 29, Issue 3, Page 2337-2362, January 2019. Recent exploration of optimal individualized decision rules (IDRs) for patients in precision medicine has attracted a lot...
By Vera Roshchina and Levent TunçelSIAM Journal on Optimization, Volume 29, Issue 3, Page 2363-2387, January 2019. We study the boundary structure of closed convex cones, with a focus on facially dual complete (nice)...
By Burak Kocuk and Diego A. Morán R.SIAM Journal on Optimization, Volume 29, Issue 3, Page 2320-2336, January 2019. In this paper, we show that the subadditive dual of a feasible conic mixed-integer program (MIP) is...
By Hedy Attouch, Zaki Chbani, and Hassan RiahiSIAM Journal on Optimization, Volume 29, Issue 3, Page 2227-2256, January 2019. In a Hilbert space setting, we consider a class of inertial proximal algorithms for nonsmooth convex optimization,...
By Immanuel M. Bomze, Francesco Rinaldi, and Samuel Rota BulòSIAM Journal on Optimization, Volume 29, Issue 3, Page 2211-2226, January 2019. In this paper, we focus on the problem of minimizing a nonconvex function over the unit simplex....
By Wim van Ackooij and Pedro Pérez-ArosSIAM Journal on Optimization, Volume 29, Issue 3, Page 2179-2210, January 2019. In decision-making problems under uncertainty, probability constraints are a valuable tool for expressing safety of decisions. They...
By Yair Carmon and John DuchiSIAM Journal on Optimization, Volume 29, Issue 3, Page 2146-2178, January 2019. We consider the minimization of a nonconvex quadratic form regularized by a cubic term, which may exhibit...
By Hilal Asi and John C. DuchiSIAM Journal on Optimization, Volume 29, Issue 3, Page 2257-2290, January 2019. We develop model-based methods for solving stochastic convex optimization problems, introducing the approximate-proximal point, or aProx, family,...
By Immanuel M. Bomze, Panayotis Mertikopoulos, Werner Schachinger, and Mathias StaudiglSIAM Journal on Optimization, Volume 29, Issue 3, Page 2100-2127, January 2019. In this paper, we propose an interior-point method for linearly constrained---and possibly nonconvex---optimization problems. The method---which we...
By Jean-Bernard Lasserre and Victor MagronSIAM Journal on Optimization, Volume 29, Issue 3, Page 2128-2145, January 2019. We interpret some incorrect results (due to numerical inaccuracies) already observed when solving semidefinite programming (SDP) relaxations...
By Jie Shen, Jie Xu, and Jiang YangSIAM Review, Volume 61, Issue 3, Page 474-506, January 2019. We propose a new numerical technique to deal with nonlinear terms in gradient flows. By introducing...
By Arjun S. Ramani, Nicole Eikmeier, and David F. GleichSIAM Review, Volume 61, Issue 3, Page 549-595, January 2019. Common models for random graphs, such as Erdös--Rényi and Kronecker graphs, correspond to generating random adjacency matrices where each...
By Johan S. H. van Leeuwaarden, Britt W. J. Mathijsen, and Bert ZwartSIAM Review, Volume 61, Issue 3, Page 403-440, January 2019. Multiserver queueing systems describe situations in which users require service from multiple parallel servers. Examples include check-in lines...
By Ben Adcock and Daan HuybrechsSIAM Review, Volume 61, Issue 3, Page 443-473, January 2019. Functions of one or more variables are usually approximated with a basis: a complete, linearly independent system of functions...
By T. J. Dodwell, C. Ketelsen, R. Scheichl, and A. L. TeckentrupSIAM Review, Volume 61, Issue 3, Page 509-545, January 2019. In this paper we address the problem of the prohibitively large computational cost of existing Markov chain Monte Carlo...
By Roger Cropp and John NorburySIAM Review, Volume 61, Issue 3, Page 596-622, January 2019. Obligate mutualist interactions appear to be ubiquitous in nature but cannot be described by the simple models that have...
By The EditorsSIAM Review, Volume 61, Issue 3, Page 507-507, January 2019. In this section we present “Multilevel Markov Chain Monte Carlo,” by T. J. Dodwell, C. Ketelsen, R. Scheichl, and...
By Volker H. SchulzSIAM Review, Volume 61, Issue 3, Page 625-640, January 2019. This time, the Book Reviews section has a large portion of books devoted to system dynamics and applications. The...
By Misha E. KilmerSIAM Review, Volume 61, Issue 3, Page 441-441, January 2019. The issue of accurate function approximation is an important topic in numerical analysis. The first Research Spotlights article,...
By J. M. Sanz-SernaSIAM Review, Volume 61, Issue 3, Page 401-401, January 2019. Johan S. H. van Leeuwaarden, Britt W. J. Mathijsen, and Bert Zwart are the authors of the Survey and...
By Darinka DentchevaSIAM Review, Volume 61, Issue 3, Page 547-548, January 2019. This issue of SIAM Review presents two papers in the Education section. The first is “Coin-Flipping, Ball-Dropping, and Grass-Hopping...
By Mila Nikolova and Pauline TanSIAM Journal on Optimization, Volume 29, Issue 3, Page 2053-2078, January 2019. There has been increasing interest in constrained nonconvex regularized block optimization problems. We introduce an approach that...
By Meixia Lin, Yong-Jin Liu, Defeng Sun, and Kim-Chuan TohSIAM Journal on Optimization, Volume 29, Issue 3, Page 2026-2052, January 2019. We focus on solving the clustered Lasso problem, which is a least squares problem with the $\ell_1$-type...
By Caroline Geiersbach and Georg Ch. PflugSIAM Journal on Optimization, Volume 29, Issue 3, Page 2079-2099, January 2019. Convergence of a projected stochastic gradient algorithm is demonstrated for convex objective functionals with convex constraint sets...
By Marie-Ange Dahito and Dominique OrbanSIAM Journal on Optimization, Volume 29, Issue 3, Page 1988-2025, January 2019. The minimum residual method (MINRES) of Paige and Saunders [SIAM J. Numer. Anal., 12 (1975), pp. 617--629],...
By Patrick R. Johnstone and Jonathan EcksteinSIAM Journal on Optimization, Volume 29, Issue 3, Page 1931-1957, January 2019. Projective splitting is a family of methods for solving inclusions involving sums of maximal monotone operators. First...
By Shuyang Ling, Ruitu Xu, and Afonso S. BandeiraSIAM Journal on Optimization, Volume 29, Issue 3, Page 1879-1907, January 2019. Studying the landscape of nonconvex cost functions is key towards a better understanding of optimization algorithms widely...
By Damek Davis and Benjamin GrimmerSIAM Journal on Optimization, Volume 29, Issue 3, Page 1908-1930, January 2019. In this paper, we introduce a stochastic projected subgradient method for weakly convex (i.e., uniformly prox-regular) nonsmooth,...
By James V. Burke, Yuan Gao, and Tim HoheiselSIAM Journal on Optimization, Volume 29, Issue 3, Page 1958-1987, January 2019. We show that many important convex matrix functions can be represented as the partial infimal projection of...
By Long-Fei Wang and Yong XiaSIAM Journal on Optimization, Volume 29, Issue 3, Page 1844-1869, January 2019. We study the problem, which we refer to as problem (P), of maximizing the sum of a...
By Dinh The Luc and Michel VolleSIAM Journal on Optimization, Volume 29, Issue 3, Page 1819-1843, January 2019. We introduce a new perturbation function for the problem of minimizing an infinite sum of functions on...
By Richard H. Byrd, Jorio Cocola, and Richard A. TapiaSIAM Journal on Optimization, Volume 29, Issue 3, Page 1870-1878, January 2019. The finite-dimensional McCormick second-order sufficiency theory for nonlinear programming problems with a finite number of constraints is...
By Min Zhang, Jie Sun, and Honglei XuSIAM Journal on Optimization, Volume 29, Issue 3, Page 1799-1818, January 2019. A model of a two-stage $N$-person noncooperative game under uncertainty is studied, in which at the first...
By Luca Calatroni and Antonin ChambolleSIAM Journal on Optimization, Volume 29, Issue 3, Page 1772-1798, January 2019. We present and analyze a backtracking strategy for a general fast iterative shrinkage/thresholding algorithm proposed by Chambolle...
By Daniel Průša and Tomáš WernerSIAM Journal on Optimization, Volume 29, Issue 3, Page 1745-1771, January 2019. We show that the general linear programming (LP) problem reduces in nearly linear time to the LP...
By A. Ridha Mahjoub, Michael Poss, Luidi Simonetti, and Eduardo UchoaSIAM Journal on Optimization, Volume 29, Issue 2, Page 1687-1713, January 2019. We propose a new generic way to construct extended formulations for a large class of network design...
By Pedro Pérez-ArosSIAM Journal on Optimization, Volume 29, Issue 2, Page 1714-1743, January 2019. This work provides calculus rules for the Fréchet and Mordukhovich subdifferentials of the pointwise supremum given by...
By Weiwei Xu, Wen Li, Lei Zhu, and Xueping HuangSIAM Journal on Optimization, Volume 29, Issue 2, Page 1657-1686, January 2019. In this paper we present the analytic solutions of the following constrained matrix determinant and trace minimization...
By Rujun Jiang and Duan LiSIAM Journal on Optimization, Volume 29, Issue 2, Page 1603-1633, January 2019. We present a new solution framework to solve the generalized trust region subproblem (GTRS) of minimizing a...
By Lorenzo Lampariello, Simone Sagratella, and Oliver SteinSIAM Journal on Optimization, Volume 29, Issue 2, Page 1634-1656, January 2019. Pessimistic bilevel optimization problems, as do optimistic ones, possess a structure involving three interrelated optimization problems. Moreover,...
By Boris Mordukhovich and Ebrahim SarabiSIAM Journal on Optimization, Volume 29, Issue 2, Page 1524-1557, January 2019. The paper concerns the study of criticality of Lagrange multipliers in variational systems, which has been recognized...
By Karima Boufi and Ahmed RoubiSIAM Journal on Optimization, Volume 29, Issue 2, Page 1578-1602, January 2019. We propose new duality results for generalized fractional programs (GFP) for a wide class of problems, not...
By D. Aussel, K. Cao Van, and D. SalasSIAM Journal on Optimization, Volume 29, Issue 2, Page 1558-1577, January 2019. Quasi-variational inequalities are variational inequalities in which the constraint map depends on the current point. Due to...
By Hongzhou Lin, Julien Mairal, and Zaid HarchaouiSIAM Journal on Optimization, Volume 29, Issue 2, Page 1408-1443, January 2019. We propose an inexact variable-metric proximal point algorithm to accelerate gradient-based optimization algorithms. The proposed scheme, called...
By Andrea Walther and Andreas GriewankSIAM Journal on Optimization, Volume 29, Issue 2, Page 1473-1501, January 2019. Functions defined by evaluation programs involving smooth elementals and absolute values as well as the max and...
By Hiroyuki Sato, Hiroyuki Kasai, and Bamdev MishraSIAM Journal on Optimization, Volume 29, Issue 2, Page 1444-1472, January 2019. In recent years, stochastic variance reduction algorithms have attracted considerable attention for minimizing the average of a...
By Phan Quoc Khanh and Nguyen Hong QuanSIAM Journal on Optimization, Volume 29, Issue 2, Page 1502-1523, January 2019. We propose an approach to a unified study of existence based on using new purely set-theoretic notions...
By Yashan XuSIAM Journal on Optimization, Volume 29, Issue 2, Page 1392-1407, January 2019. In this paper, we consider a two-person zero-sum game problem for an elliptic variational inequality in which...
By Michael P. Friedlander, Ives Macêdo, and Ting Kei PongSIAM Journal on Optimization, Volume 29, Issue 2, Page 1366-1391, January 2019. The Moreau envelope is one of the key convexity-preserving functional operations in convex analysis, and it is...
By Radu Ioan Boţ, Ernö Robert Csetnek, and Dang-Khoa NguyenSIAM Journal on Optimization, Volume 29, Issue 2, Page 1300-1328, January 2019. We propose a proximal algorithm for minimizing objective functions consisting of three summands: the composition of a...
By Benjamin GrimmerSIAM Journal on Optimization, Volume 29, Issue 2, Page 1350-1365, January 2019. We extend the classic convergence rate theory for subgradient methods to apply to non-Lipschitz functions. For the...
By Armin Eftekhari and Andrew ThompsonSIAM Journal on Optimization, Volume 29, Issue 2, Page 1329-1349, January 2019. We study an optimization program over nonnegative Borel measures that encourages sparsity in its solution. Efficient solvers...
By Paul T. Boggs and Richard H. ByrdSIAM Journal on Optimization, Volume 29, Issue 2, Page 1282-1299, January 2019. The limited-memory BFGS method (L-BFGS) has become the workhorse optimization strategy for many large-scale nonlinear optimization problems....
By Claire Boyer, Antonin Chambolle, Yohann De Castro, Vincent Duval, Frédéric de Gournay, and Pierre WeissSIAM Journal on Optimization, Volume 29, Issue 2, Page 1260-1281, January 2019. We establish a general principle which states that regularizing an inverse problem with a convex function yields...
By Dan Wilson and Bard ErmentroutSIAM Review, Volume 61, Issue 2, Page 277-315, January 2019. While phase reduction is a tremendously useful tool for understanding the dynamics of weakly perturbed limit cycle oscillators,...
By Bernhard Beckermann and Alex TownsendSIAM Review, Volume 61, Issue 2, Page 319-344, January 2019. Matrices with displacement structure, such as Pick, Vandermonde, and Hankel matrices, appear in a diverse range of applications. In...
By Samuel Awoniyi and Ira WheatonSIAM Review, Volume 61, Issue 2, Page 347-360, January 2019. This education article presents a case for first courses on Markov chain modeling to include the topic “sojourn time...
By Guodong Shi, Claudio Altafini, and John S. BarasSIAM Review, Volume 61, Issue 2, Page 229-257, January 2019. A signed network is a network in which each link is associated with a positive or negative sign....
By Richard D. NeidingerSIAM Review, Volume 61, Issue 2, Page 361-381, January 2019. Techniques of univariate Newton interpolating polynomials are extended to multivariate data points by different generalizations and practical algorithms. ...
By Beate Ehrhardt and Patrick J. WolfeSIAM Review, Volume 61, Issue 2, Page 261-276, January 2019. We characterize the large-sample properties of network modularity in the presence of covariates, under a natural and flexible null...
By Misha E. KilmerSIAM Review, Volume 61, Issue 2, Page 259-259, January 2019. The paper “Network Modularity in the Presence of Covariates," by Beate Ehrhardt and Patrick J. Wolfe, is the first...
By Darinka DentchevaSIAM Review, Volume 61, Issue 2, Page 345-346, January 2019. This issue of SIAM Review contains two papers in the Education section. The first paper is “Case for First...
By J. M. Sanz-SernaSIAM Review, Volume 61, Issue 2, Page 227-227, January 2019. ``Dynamics over Signed Networks,” by Guodong Shi, Claudio Altafini, and John S. Baras, is the Survey and Review article...
By Volker H. SchulzSIAM Review, Volume 61, Issue 2, Page 385-400, January 2019. Sometimes, people confuse artificial neural networks (ANNs) with real biological brains, at least metaphorically. The section starts with a...
By The EditorsSIAM Review, Volume 61, Issue 2, Page 317-317, January 2019. The SIGEST article in this issue is “Bounds on the Singular Values of Matrices with Displacement Structure,” by Bernhard...
By Luze Xu, Jon Lee, and Daphne SkipperSIAM Journal on Optimization, Volume 29, Issue 2, Page 1240-1259, January 2019. In the context of global optimization of mixed-integer nonlinear optimization formulations, we consider smoothing univariate functions $f$...
By Napat Rujeerapaiboon, Kilian Schindler, Daniel Kuhn, and Wolfram WiesemannSIAM Journal on Optimization, Volume 29, Issue 2, Page 1211-1239, January 2019. Plain vanilla $K$-means clustering has proven to be successful in practice, yet it suffers from outlier sensitivity...
By Amitabh Basu and Sriram SankaranarayananSIAM Journal on Optimization, Volume 29, Issue 2, Page 1190-1210, January 2019. Making cut-generating functions (CGFs) computationally viable is a central question in modern integer programming research. One would...
By Charles Audet, Sébastien Le Digabel, and Christophe TribesSIAM Journal on Optimization, Volume 29, Issue 2, Page 1164-1189, January 2019. The mesh adaptive direct search (Mads) algorithm is designed for blackbox optimization problems for which the functions...
By Rafael Correa, Abderrahim Hantoute, and Marco A. López-CerdáSIAM Journal on Optimization, Volume 29, Issue 2, Page 1106-1130, January 2019. We characterize the subdifferential of the supremum function of finitely and infinitely indexed families of convex functions....
By Pierre Bonami, Andrea Lodi, Jonas Schweiger, and Andrea TramontaniSIAM Journal on Optimization, Volume 29, Issue 2, Page 1076-1105, January 2019. We propose new cutting planes for strengthening the linear relaxations that appear in the solution of nonconvex...
By Nir Halman and Giacomo NanniciniSIAM Journal on Optimization, Volume 29, Issue 2, Page 1131-1163, January 2019. We propose a fully polynomial-time approximation scheme (FPTAS) for stochastic dynamic programs with multidimensional action, scalar state,...
By Cosmin G. Petra, Naiyuan Chiang, and Mihai AnitescuSIAM Journal on Optimization, Volume 29, Issue 2, Page 1048-1075, January 2019. We present a structured quasi-Newton algorithm for unconstrained optimization problems that have unavailable second-order derivatives or Hessian...
By Jana Thomann and Gabriele EichfelderSIAM Journal on Optimization, Volume 29, Issue 2, Page 1017-1047, January 2019. This paper presents a trust-region method for multiobjective heterogeneous optimization problems. One of the objective functions is...
By Albert S. Berahas, Richard H. Byrd, and Jorge NocedalSIAM Journal on Optimization, Volume 29, Issue 2, Page 965-993, January 2019. This paper presents a finite-difference quasi-Newton method for the minimization of noisy functions. The method takes advantage...
By Aram V. Arutyunov and Sergey E. ZhukovskiySIAM Journal on Optimization, Volume 29, Issue 2, Page 994-1016, January 2019. Functions defined on metric spaces are studied. For these functions, a generalized Caristi-like condition is introduced. It...
By Christian Clason, Stanislav Mazurenko, and Tuomo ValkonenSIAM Journal on Optimization, Volume 29, Issue 1, Page 933-963, January 2019. The primal-dual hybrid gradient method, modified (PDHGM, also known as the Chambolle--Pock method), has proved very successful...
By Xiaojun Chen, Ph. L. Toint, and H. WangSIAM Journal on Optimization, Volume 29, Issue 1, Page 874-903, January 2019. An adaptive regularization algorithm using high-order models is proposed for solving partially separable convexly constrained nonlinear optimization...
By Man-Chung Yue, Zirui Zhou, and Anthony Man-Cho SoSIAM Journal on Optimization, Volume 29, Issue 1, Page 904-932, January 2019. In this paper we consider the cubic regularization (CR) method, a regularized version of the classical Newton...
By Julia Niebling and Gabriele EichfelderSIAM Journal on Optimization, Volume 29, Issue 1, Page 794-821, January 2019. A new branch--and--bound-based algorithm for smooth nonconvex multiobjective optimization problems with convex constraints is presented. The algorithm...
By Alperen A. ErgürSIAM Journal on Optimization, Volume 29, Issue 1, Page 852-873, January 2019. We study polyhedral approximations to the cone of nonnegative polynomials. We show that any constant ratio polyhedral...
By Dávid Papp and Sercan YildizSIAM Journal on Optimization, Volume 29, Issue 1, Page 822-851, January 2019. We propose a homogeneous primal-dual interior-point method to solve sum-of-squares optimization problems by combining nonsymmetric conic optimization...
By C. Kanzow, V. Karl, D. Steck, and D. WachsmuthSIAM Journal on Optimization, Volume 29, Issue 1, Page 767-793, January 2019. This paper deals with generalized Nash equilibrium problems (GNEPs) in Banach spaces. We give an existence result...
By Roberto Andreani, Nadia S. Fazzio, Maria L. Schuverdt, and Leonardo D. SecchinSIAM Journal on Optimization, Volume 29, Issue 1, Page 743-766, January 2019. In the present paper, we prove that the augmented Lagrangian method converges to KKT points under the...
By Shuoguang Yang, Mengdi Wang, and Ethan X. FangSIAM Journal on Optimization, Volume 29, Issue 1, Page 616-659, January 2019. Stochastic gradient methods are scalable for solving large-scale optimization problems that involve empirical expectations of loss functions....
By Hao-Hsiang Wu and Si̇mge KüçükyavuzSIAM Journal on Optimization, Volume 29, Issue 1, Page 690-718, January 2019. We consider a class of chance-constrained combinatorial optimization problems, which we refer to as probabilistic partial set-covering...
By Jelena Diakonikolas and Lorenzo OrecchiaSIAM Journal on Optimization, Volume 29, Issue 1, Page 660-689, January 2019. We present a general technique for the analysis of first-order methods. The technique relies on the construction...
By Cheng Lu, Ya-Feng Liu, Wei-Qiang Zhang, and Shuzhong ZhangSIAM Journal on Optimization, Volume 29, Issue 1, Page 719-742, January 2019. In this paper, we consider a fundamental problem in modern digital communications known as multiple-input multiple-output (MIMO)...
By Coralia Cartis, Nick I. Gould, and Philippe L. TointSIAM Journal on Optimization, Volume 29, Issue 1, Page 595-615, January 2019. Adaptive cubic regularization methods have emerged as a credible alternative to linesearch and trust-region for smooth nonconvex...
By Liqiang Song and Wei Hong YangSIAM Journal on Optimization, Volume 29, Issue 1, Page 571-594, January 2019. We present a block Lanczos method for solving the large-scale extended trust-region (ETR) subproblem. During the algorithm,...
By Jean-Paul PenotSIAM Journal on Optimization, Volume 29, Issue 1, Page 522-540, January 2019. We consider minimization problems in which the constraints are defined by set-valued maps. Such problems appear when...
By Peter OchsSIAM Journal on Optimization, Volume 29, Issue 1, Page 541-570, January 2019. An abstract convergence theorem for a class of generalized descent methods that explicitly models relative errors is...
By C. H. Jeffrey PangSIAM Journal on Optimization, Volume 29, Issue 1, Page 484-510, January 2019. Consider a setting where each vertex of a graph has a function, and communications can only occur...
By Aris Daniilidis and Gonzalo FloresSIAM Journal on Optimization, Volume 29, Issue 1, Page 511-521, January 2019. In this paper we establish that the set of Lipschitz functions $f:\mathcal{U}\rightarrow \mathbb{R}$ ($\mathcal{U}$ a nonempty open...
By Martin J. Gander and Hui ZhangSIAM Review, Volume 61, Issue 1, Page 3-76, January 2019. Solving time-harmonic wave propagation problems by iterative methods is a difficult task, and over the last two decades an...
By Eddie Nijholt, Bob Rink, and Jan SandersSIAM Review, Volume 61, Issue 1, Page 121-155, January 2019. Many systems in science and technology are networks: they consist of nodes with connections between them. Examples include electronic...
By Gjerrit MeinsmaSIAM Review, Volume 61, Issue 1, Page 159-184, January 2019. A complete theory of dimensional and scaling analysis is presented and its power is demonstrated through a series of...
By Francesca Maggioni and Georg Ch. PflugSIAM Journal on Optimization, Volume 29, Issue 1, Page 454-483, January 2019. In general, multistage stochastic optimization problems are formulated on the basis of continuous distributions describing the uncertainty....
By Simone Carlo Surace, Anna Kutschireiter, and Jean-Pascal PfisterSIAM Review, Volume 61, Issue 1, Page 79-91, January 2019. Particle filters are a popular and flexible class of numerical algorithms to solve a large class of nonlinear filtering...
By Peter G. Fennell and James P. GleesonSIAM Review, Volume 61, Issue 1, Page 92-118, January 2019. Multistate dynamical processes on networks, where nodes can occupy one of a multitude of discrete states, are gaining widespread...
By Helmut Gfrerer and Boris S. MordukhovichSIAM Journal on Optimization, Volume 29, Issue 1, Page 423-453, January 2019. This paper is devoted to the generalized differential study of the normal cone mappings associated with a...
By Silviu Filip, Aurya Javeed, and Lloyd N. TrefethenSIAM Review, Volume 61, Issue 1, Page 185-205, January 2019. The usual way in which mathematicians work with randomness is by a rigorous formulation of the idea of Brownian...
By Darinka DentchevaSIAM Review, Volume 61, Issue 1, Page 157-158, January 2019. In this issue, the Education section of SIAM Review presents two papers. The first is “Dimensional and Scaling Analysis,”...
By Misha E. KilmerSIAM Review, Volume 61, Issue 1, Page 77-77, January 2019. Short and to the point, “How to Avoid the Curse of Dimensionality: Scalability of Particle Filters with and...
By Volker H. SchulzSIAM Review, Volume 61, Issue 1, Page 209-225, January 2019. This time, the Book Reviews section has a large portion of reviews on books devoted to applications---in particular medical...
By J. M. Sanz-SernaSIAM Review, Volume 61, Issue 1, Page 1-1, January 2019. The Survey and Review article in this issue is “A Class of Iterative Solvers for the Helmholtz Equation: Factorizations,...
By The EditorsSIAM Review, Volume 61, Issue 1, Page 119-119, January 2019. Over the last fifteen years, SIAM Review has published a range of articles in the general area of network...
By Amir Ali Ahmadi, Etienne de Klerk, and Georgina HallSIAM Journal on Optimization, Volume 29, Issue 1, Page 399-422, January 2019. In this paper, we study polynomial norms, i.e., norms that are the $d$th root of a degree-$d$...
By Daniel Rehfeldt and Thorsten KochSIAM Journal on Optimization, Volume 29, Issue 1, Page 369-398, January 2019. Borne out of a surprising variety of practical applications, the maximum-weight connected subgraph problem has attracted considerable...
By Santiago Paternain, Aryan Mokhtari, and Alejandro RibeiroSIAM Journal on Optimization, Volume 29, Issue 1, Page 343-368, January 2019. Machine learning problems such as neural network training, tensor decomposition, and matrix factorization require local minimization of...
By Kibaek Kim, Cosmin G. Petra, and Victor M. ZavalaSIAM Journal on Optimization, Volume 29, Issue 1, Page 318-342, January 2019. We present an asynchronous bundle-trust-region algorithm within the context of Lagrangian dual decomposition for stochastic mixed-integer programs....
By Christian Hess and Raffaello SeriSIAM Journal on Optimization, Volume 29, Issue 1, Page 290-317, January 2019. In stochastic programming, statistics, or econometrics, the aim is in general the optimization of a criterion function...
By Xujin Chen, Wenan Zang, and Qiulan ZhaoSIAM Journal on Optimization, Volume 29, Issue 1, Page 240-261, January 2019. Given a multigraph $G=(V,E)$ with a positive rational weight $w(e)$ on each edge $e$, the weighted density...
By Andreas Griewank and Andrea WaltherSIAM Journal on Optimization, Volume 29, Issue 1, Page 262-289, January 2019. In the paper [Optim. Methods Softw., 31 (2016), pp. 904--930] we derived first order (KKT) and second...
By Damek Davis and Dmitriy DrusvyatskiySIAM Journal on Optimization, Volume 29, Issue 1, Page 207-239, January 2019. We consider a family of algorithms that successively sample and minimize simple stochastic models of the objective...
By Xiaojun Chen, Alexander Shapiro, and Hailin SunSIAM Journal on Optimization, Volume 29, Issue 1, Page 135-161, January 2019. A solution of two-stage stochastic generalized equations is a pair: a first stage solution which is independent...
By David H. Gutman and Javier F. Pen͂aSIAM Journal on Optimization, Volume 29, Issue 1, Page 162-174, January 2019. We give a novel proof of the ${{\mathcal O}}(1/k)$ and ${{\mathcal O}}(1/k^2)$ convergence rates of the proximal...
By Alfredo N. Iusem, Alejandro Jofré, Roberto I. Oliveira, and Philip ThompsonSIAM Journal on Optimization, Volume 29, Issue 1, Page 175-206, January 2019. In this paper, we propose dynamic sampled stochastic approximated (DS-SA) extragradient methods for stochastic variational inequalities (SVIs)...
By Olivier Fercoq and Pascal BianchiSIAM Journal on Optimization, Volume 29, Issue 1, Page 100-134, January 2019. This paper introduces a randomized coordinate-descent version of the Vu͂--Condat algorithm. By coordinate descent, we mean that...
By Geovani N. Grapiglia and Yurii NesterovSIAM Journal on Optimization, Volume 29, Issue 1, Page 77-99, January 2019. In this paper, we study accelerated regularized Newton methods for minimizing objectives formed as a sum of...
By Merve Bodur, Alberto Del Pia, Santanu S. Dey, and Marco MolinaroSIAM Journal on Optimization, Volume 29, Issue 1, Page 55-76, January 2019. In this paper, we present lower bounds on the rank of the split closure, the multi-branch closure,...
By Luís Felipe Bueno, Gabriel Haeser, and Frank Navarro RojasSIAM Journal on Optimization, Volume 29, Issue 1, Page 31-54, January 2019. Generalized Nash equilibrium problems (GNEPs) are a generalization of the classic Nash equilibrium problems (NEPs), where each...
By Daniel Blado and Alejandro TorielloSIAM Journal on Optimization, Volume 29, Issue 1, Page 1-30, January 2019. We consider a version of the knapsack problem in which an item size is random and revealed...
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The SIAM Conference on Optimization will feature the latest research on the theory, algorithms, software, and applications of optimization. A particular emphasis will be put on applications of ...
By Lina Sorg The southern pine is a commercially-important species of tree that grows along Australia’s Fraser coast in southeast Queensland and parts of northern New South Wales. The trees ...
By Michael L. Overton Andrew R. Conn, ...
The SIAM Student Paper Prize is awarded annually to the student author(s) of the most outstanding paper(s) accepted by SIAM journals within the three years preceding the nomination deadline. Starting with the 2018 award, the focus of the prize is to recognize outstanding scholarship by students in SIAM journals.
Established in 1979 in honor of James H. Wilkinson, the prize is awarded to an individual in their early career for recent contributions to numerical analysis and scientific computing.
The prize honors George B. Dantzig for his contributions to operations research and computer science. The prize is awarded every three years to one or more individuals for original research which by its originality, breadth, and depth is having a major impact on the field of mathematical optimization. Nominations are collected via the Mathematical Optimization Society (MOS) website.
The Lagrange Prize in Continuous Optimization is awarded every three years by the Mathematical Optimization Society (MOS) and SIAM for an outstanding contribution in the area of continuous optimization published in the six calendar years preceding the award year. Nominations can be submitted via the MOS website.
The Pioneer Prize is awarded every four years at the International Council for Industrial and Applied Mathematics (ICIAM) Congress to one individual for pioneering work introducing applied mathematical methods and scientific computing techniques to an industrial problem area or a new scientific field of applications. Nominations can be submitted via the ICIAM website.
The prize recognizes innovative software in scientific computing by researchers in the earlier stages of their career. Starting in 2019, SIAM will award the prize every four years at the SIAM Conference on Computational Science and Engineering.
This joint prize was established in 2002 to honor Sonia Kovalevsky and her work on the theory of differential equations. It is awarded to anyone in the scientific or engineering community whose work highlights the achievements of women in applied and computational mathematics. Nominations can be submitted via the AWM website.
This prize was created in 2013 to emphasize George Pólya’s legacy of communicating mathematics effectively. It joins two long-standing Pólya prizes SIAM has awarded in combinatorics and other fields beginning in 1969.
Through the generosity and inspiration of Gerald and Judith Porter, the Mathematical Association of America (MAA), American Mathematical Society (AMS), and SIAM offer this annual lecture at the Joint Mathematics Meetings. The lecture, first awarded in 2010, is given on a mathematical topic accessible to the broader community.
Named in honor of I. E. Block, a co-founder and the first managing director of SIAM, this lecture is open to the public at the SIAM Annual Meeting. It is intended to encourage public appreciation of applied mathematics and computational science by reaching out to the local community.
Established in 1959, the prize honors John von Neumann, a founder of modern computing. The prize is awarded annually for distinguished contributions to applied mathematics and for the effective communication of these ideas to the community.
The JPBM Communications Award is given annually to reward and encourage communicators who, on a sustained basis, bring mathematical ideas and information to non-mathematical audiences. The prize may be awarded in two categories: For Public Outreach and For Expository and Popular Books. Nominations can be submitted via the AMS website.
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Established in 1998 in memory of Ralph E. Kleinman, the prize recognizes contributions that bridge the gap between high-level mathematics and engineering problems. The award is based on the quality and impact of the mathematics.
The prize was established in 1986 in memory of Richard C. DiPrima, who served SIAM for many years and in 1979–1980 as SIAM President. It aims to recognize an early career researcher in applied mathematics and is based on the doctoral dissertation.
Established in 1992, this prize is awarded to the author or authors of the most outstanding paper, as determined by the selection committee, on a topic in optimization.
Established in 2018, the prize is awarded every three years to an individual in their early career for outstanding, influential, and potentially long-lasting contributions to the field of optimization.
The prize recognizes students for outstanding solutions to real world math problems. It is awarded to two of the teams judged "Outstanding" in the Mathematical Contest in Modeling (MCM) administered annually by the Consortium for Mathematics and Its Applications (COMAP). Registration is accepted via the COMAP website.
The SIAM Outstanding Paper Prizes bring attention to papers published in SIAM journals. Three awards are made each year to the authors of papers deemed by SIAM journal editors-in-chief worthy of particular attention.
The prize, established in 1985 and originally intended to be awarded periodically, is now awarded annually for contributions to the advancement of applied mathematics on the national or international level.
Looking for financial support to further your research? Fellowships often provide funding plus experiential learning opportunities to young researchers. Learn more about fellowship opportunities.
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Internships allow you to network and forge connections for future job possibilities, while also exploring possible areas of interest. Look at this list of companies who offer valuable opportunities.
Our community is founded on igniting groundbreaking developments in applied math and computational science. Take a deeper dive into your area of study with one of these opportunities.