The TCS Formalization Drive

Routing several commodities through one capacitated network at the same time is a basic problem of network optimization, with applications to communication networks, VLSI layout and divide-and-conquer graph algorithms. Its optimum is the value of a linear program, but the natural certificates of an upper bound are combinatorial: every cut of the network…

Online matching describes allocation when requests must be answered as they arrive. A matching decision uses only the edges revealed so far and cannot be revised when later requests appear. In the bipartite setting studied by Karp, U. Vazirani, and V. Vazirani, the arriving vertices are girls and the possible partners are boys. Even when the full graph has…

Prove that subset sum is NP-complete by reducing the existing 3-SAT language, to it. Given a list of positive integers and a nonnegative target, decide whether some selection of entries sums exactly to the target. Integers are encoded in binary.

Sparse graphs whose adjacency spectrum is concentrated near zero, expanders, are used throughout theoretical computer science: in error-correcting codes, derandomization, sorting and routing networks, and the construction of pseudorandom objects (Hoory, Linial and Wigderson, survey). The best possible spectral expansion for a $d$-regular graph is governed by…

Prove that $k$-SAT is NP-complete for every fixed $k\ge3$, with at most $k$ literal occurrences per clause.

Learning an unknown quantum state is expensive. Full quantum state tomography of a $D$-dimensional mixed state $\rho$ to accuracy $\varepsilon$ in trace distance needs on the order of $D^2/\varepsilon^2$ copies of $\rho$ (O'Donnell–Wright 2016; Haah et al. 2017), and this is optimal. For a system of $n$ qubits, $D = 2^n$, so full tomography is out of reach beyond a few…

Online retailers choose, customer by customer, which products to display. The customer's type (location, history, device) is revealed on arrival. Inventories are finite and are not replenished during the selling season. A seller who shows every customer the currently most profitable products can sell out a product early and leave nothing for later customers…

Online linear programming models resource allocation under uncertainty about future demand: requests arrive one at a time, each asking for some amounts of $m$ resources and offering a payment, and each must be accepted or rejected, irrevocably, on arrival. Airline seat and hotel room revenue management, online routing with bandwidth limits and the allocation…

Caching (online paging) is one of the oldest problems in online algorithms: a fast memory of $k$ slots serves a sequence of requests, and every request for an element not in the fast memory is a cache miss that forces the element to be loaded, possibly evicting another one. With the whole request sequence known in advance, evicting the element whose next…

The $k$-server problem is a basic model of on-line decision making. $k$ mobile servers move in a metric space, requests for points arrive one at a time, and each request has to be covered by a server before the next one arrives. The cost is the total distance the servers move. The problem includes paging, caching and disk-head scheduling as special cases (Manass…

In a shared-memory multiprocessor, each processor keeps copies of memory blocks in its own cache, and all caches listen ("snoop") on a common bus. Every bus cycle spent keeping these copies consistent is a cycle not available for useful work, so the protocol that decides when a block is shared by several caches and when it is private to one cache directly…

Many equivalence problems on finite structures reduce to computing the coarsest partition of a finite set that is compatible with a relation. Deciding whether two states of a finite labelled transition system are bisimilar, testing congruence of finite-state processes in Milner's calculus of communicating systems (CCS), and minimizing a deterministic finite…

Paging is the problem of managing a two-level memory: a fast cache holds $k$ pages out of an address space of $n$ pages, requests to pages arrive one at a time, and a request to a page outside the cache (a page fault) forces the algorithm to bring that page in and, when the cache is full, to evict another. The cost is the number of faults. An on-line algorithm…

The traveling salesman problem with the triangle inequality asks for a shortest closed tour through $n$ points whose distances form a metric. It is NP-hard, so in practice tours are built by fast construction heuristics, and the natural question is how far such a tour can be from optimal in the worst case. Rosenkrantz, Stearns and Lewis (SIAM J. Comput. 6(3),…

Bin packing asks how to place a list of items with sizes in $(0,1]$ into as few unit-capacity bins as possible. It models the cutting of stock material, the packing of files onto tracks of a disc and the assignment of jobs to machines with a common deadline. Deciding the optimum is NP-hard, so in practice simple rules are used, and the question is how far…

Bin packing asks for the fewest unit-capacity bins that hold a given list of item sizes. It models cutting stock, memory allocation, file placement and the loading of trucks, and it is NP-hard, so in practice lists are packed by simple rules that look at one item at a time. The two most widely used rules are First-Fit and Best-Fit, and the question that…

Bin packing asks for the fewest unit-capacity bins that hold a given list of item sizes. It is one of the first problems studied through the worst-case analysis of approximation algorithms, and it models storage allocation, paging and file placement on tracks, as well as cutting-stock problems in operations research. Deciding the optimum exactly is NP-hard,…

Minimizing the average completion time of jobs that arrive over time is one of the basic objectives of machine scheduling: it measures how long, on average, a job waits in the system. On a single machine with release dates and no preemption (written $1|r_j|\sum C_j$), the problem is strongly NP-hard, so research has focused on approximation algorithms whose…

The uncapacitated facility location (UFL) problem is one of the basic models of location theory and operations research: a firm chooses which warehouses, plants or servers to open, paying a fixed cost for each open site and a service cost for every client according to its distance to the nearest open site. It is also a standard test case for approximation…

The group Steiner tree problem generalizes the ordinary Steiner tree problem: given a rooted tree and several groups of vertices, find a minimum-cost subtree that connects at least one vertex of each group to the root. It is a canonical instance of the generalized-connectivity family this survey studies in Chapter 11 — a family that also contains the online…

Section 4 of this survey derives a simple randomized $O(\log m \log n)$-competitive algorithm for the online set-cover problem, by rounding the fractional solution the online packing-covering framework produces. An intriguing question the survey poses next: can the same guarantee be achieved deterministically? The standard tool for removing randomness, the…

Maximum entropy (Maxent) models are a widely used family of density-estimation algorithms: given a sample and a set of features, they select the distribution that matches the empirical feature averages while being otherwise as "agnostic" (close to a prior, usually uniform) as possible — a principle that, notably, never requires specifying a parametric…