Universal gradient methods for convex optimization problems

In this paper, we present new methods for black-box convex minimization. They do not need to know in advance the actual level of smoothness of the objective function. Their only essential input parameter is the required accuracy of the solution. At the same time, for each particular problem class they automatically ensure the best possible … Read more

An inexact proximal bundle method with applications to convex conic programming

We present an inexact bundle method for minimizing an unconstrained convex sup-function with an open domain. Under some mild assumptions, we reformulate a convex conic programming problem as such problem in terms of the support function. This method is a first-order method, hence it requires much less computational cost in each iteration than second-order approaches … Read more

An exact tree projection algorithm for wavelets

We propose a dynamic programming algorithm for projection onto wavelet tree structures. In contrast to other recently proposed algorithms which only give approximate tree projections for a given sparsity, our algorithm is guaranteed to calculate the projection exactly. We also prove that our algorithm has O(Nk) complexity, where N is the signal dimension and k … Read more

On Finding a Generalized Lowest Rank Solution to a Linear Semi-definite Feasibility Problem

In this note, we generalize the affine rank minimization problem and the vector cardinality minimization problem and show that the resulting generalized problem can be solved by solving a sequence of continuous concave minimization problems. In the case of the vector cardinality minimization problem, we show that it can be solved by solving the continuous … Read more

Solving the High School Timetabling Problem to optimality by using ILS algorithms

The high school timetabling is a classical problem and has many combinatorial variations. It is NP-Complete and since the use of exact methods for this problem is restricted, heuristics are usually employed. This paper applies three Iterated Local Search (ILS) algorithms which includes two newly proposed neighborhood operators to heuristically solve a benchmark of the … Read more

Equipment Selection for Surface Mining: A Review

One of the challenging problems for surface mining operation optimization is choosing the optimal truck and loader fleet. This problem is the Equipment Selection Problem (ESP). In this paper, we describe the ESP in the context of surface mining. We discuss related problems and applications. Within the scope of both the ESP and related problems, … Read more

Alternating active-phase algorithm for multimaterial topology optimization problems — a 115-line MATLAB implementation

A new algorithm for the solution of multimaterial topology optimization problems is introduced in the present study. The presented method is based on the splitting of a multiphase topology optimization problem into a series of binary phase topology optimization sub-problems which are solved partially, in a sequential manner, using a traditional binary phase topology optimization … Read more

Interdiction Games on Markovian PERT Networks

In a stochastic interdiction game a proliferator aims to minimize the expected duration of a nuclear weapons development project, while an interdictor endeavors to maximize the project duration by delaying some of the project tasks. We formulate static and dynamic versions of the interdictor’s decision problem where the interdiction plan is either pre-committed or adapts … Read more

On the sufficiency of finite support duals in semi-infinite linear programming

We consider semi-infinite linear programs with countably many constraints indexed by the natural numbers. When the constraint space is the vector space of all real valued sequences, we show the finite support (Haar) dual is equivalent to the algebraic Lagrangian dual of the linear program. This settles a question left open by Anderson and Nash~\cite{anderson-nash}. … Read more

A doubly stabilized bundle method for nonsmooth convex optimization

We propose a bundle method for minimizing nonsmooth convex functions that combines both the level and the proximal stabilizations. Most bundle algorithms use a cutting-plane model of the objective function to formulate a subproblem whose solution gives the next iterate. Proximal bundle methods employ the model in the objective function of the subproblem, while level … Read more